A Novel Approach for Establishing Connectivity in Partitioned Mobile Sensor Networks using Beamforming Techniques
محورهای موضوعی : Wireless Network
Abbas Mirzaei
1
,
Shahram Zandian
2
1 - Department of Computer Engineering, Ardabil Branch, Islamic Azad University, Ardabil, Iran
2 - Department of Computer Engineering, Ardabil Branch, Islamic Azad University, Ardabil, Iran
کلید واژه: Mobile Sensor Networks (MSNs), Connectivity Restoration, Network Partitioning, Cooperative Beamforming, Fault Recovery,
چکیده مقاله :
Network connectivity is one of the major design issues in the context of mobile sensor networks. Due to diverse communication patterns, some nodes lying in high-traffic zones may consume more energy and eventually die out resulting in network partitioning. This phenomenon may deprive a large number of alive nodes of sending their important time critical data to the sink. The application of data caching in mobile sensor networks is exponentially increasing as a high-speed data storage layer. This paper presents a deep learning-based beamforming approach to find the optimal transmission strategies for cache-enabled backhaul networks. In the proposed scheme, the sensor nodes in isolated partitions work together to form a directional beam which significantly increases their overall communication range to reach out a distant relay node connected to the main part of the network. The proposed methodology of cooperative beamforming-based partition connectivity works efficiently if an isolated cluster gets partitioned with a favorably large number of nodes. We also present a new cross-layer method for link cost that makes a balance between the energy used by the relay. By directly adding the accessible auxiliary nodes to the set of routing links, the algorithm chooses paths which provide maximum dynamic beamforming usage for the intermediate nodes. The proposed approach is then evaluated through simulation results. The simulation results show that the proposed mechanism achieves up to 30% energy consumption reduction through beamforming as partition healing in addition to guarantee user throughput.
Network connectivity is one of the major design issues in the context of mobile sensor networks. Due to diverse communication patterns, some nodes lying in high-traffic zones may consume more energy and eventually die out resulting in network partitioning. This phenomenon may deprive a large number of alive nodes of sending their important time critical data to the sink. The application of data caching in mobile sensor networks is exponentially increasing as a high-speed data storage layer. This paper presents a deep learning-based beamforming approach to find the optimal transmission strategies for cache-enabled backhaul networks. In the proposed scheme, the sensor nodes in isolated partitions work together to form a directional beam which significantly increases their overall communication range to reach out a distant relay node connected to the main part of the network. The proposed methodology of cooperative beamforming-based partition connectivity works efficiently if an isolated cluster gets partitioned with a favorably large number of nodes. We also present a new cross-layer method for link cost that makes a balance between the energy used by the relay. By directly adding the accessible auxiliary nodes to the set of routing links, the algorithm chooses paths which provide maximum dynamic beamforming usage for the intermediate nodes. The proposed approach is then evaluated through simulation results. The simulation results show that the proposed mechanism achieves up to 30% energy consumption reduction through beamforming as partition healing in addition to guarantee user throughput.
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http://jist.acecr.org ISSN 2322-1437 / EISSN:2345-2773 |
Journal of Information Systems and Telecommunication
|
Abstract
Network connectivity is one of the major design issues in the context of mobile sensor networks. Due to diverse communication patterns, some nodes lying in high-traffic zones may consume more energy and eventually die out resulting in network partitioning. This phenomenon may deprive a large number of alive nodes of sending their important time critical data to the sink. The application of data caching in mobile sensor networks is exponentially increasing as a high-speed data storage layer. This paper presents a deep learning-based beamforming approach to find the optimal transmission strategies for cache-enabled backhaul networks. In the proposed scheme, the sensor nodes in isolated partitions work together to form a directional beam which significantly increases their overall communication range to reach out a distant relay node connected to the main part of the network. The proposed methodology of cooperative beamforming-based partition connectivity works efficiently if an isolated cluster gets partitioned with a favorably large number of nodes. We also present a new cross-layer method for link cost that makes a balance between the energy used by the relay. By directly adding the accessible auxiliary nodes to the set of routing links, the algorithm chooses paths which provide maximum dynamic beamforming usage for the intermediate nodes. The proposed approach is then evaluated through simulation results. The simulation results show that the proposed mechanism achieves up to 30% energy consumption reduction through beamforming as partition healing in addition to guarantee user throughput.
Keywords: Mobile Sensor Networks (MSNs); Connectivity Restoration; Network Partitioning; Cooperative Beamforming; Fault Recovery.
1- Introduction
Mobile sensor networks (MSN) are effective platforms for the industrial and military communications which are applied for commercial affairs of the manufacturing sector and the identification of enemy frontiers in the military. In most applications, sensors have the role of a data source and send information from event triggers to each eNodeB or central receiver. The unique role of the base station makes it a natural target for the enemies who intend to carry out the deadliest attack with the least possible effort against the mobile sensor network. Even if the mobile sensor network uses common security mechanisms such as encryption and authentication, the enemy may use traffic analysis techniques to identify the base station. However, the attractiveness of mobile sensor networks and their advantages make them vulnerable to potential attack by the evil enemy. A typical mobile sensor network consists of several relays that iteratively transfer new information to the existing BS. In this model, because the unique role of the base station makes it possible to carry out the most effective attack against the target mobile sensor network with the least possible effort, this station becomes the center of enemy attacks. That is, the enemy assumes that a Denial of Service (DoS) attack against the base station will actually cripple the larger mobile sensor network, because the base station not only acts as a data well.
One of the main effective ways to protect a base station from a vicious enemy attack is to keep its role, identity, and location unknown. However, conventional security mechanisms that provide confidentiality, integrity, and authentication are not capable of this type of protection [1] [2]. One of the major portions of the studies relevant to anonymous communications were so far related to analyzing routing algorithms with the aim of concealing actual paths from the transmitter well [3], [4]. It should be noted that, in spite of the fact that secure routing algorithms can greatly reduce path discovery attack, the enemy can gain important data via monitoring the link layer and the relevance between pairs of nodes, based on which it can identify the location and role of the base station [5] [6].
According to [7] [8], the authors suggested an approach in the lower layer which uses dynamic beamforming to further identify the base station. Nowadays, distributed beamforming seems a very attractive way to improve the network performance, throughput and power utility, provide data link safety, in addition to increasing SINR in the multi-layer cooperative systems [9] [10]. Based on dynamic beam modulation, several mobile sensor network nodes work together to share the existing propagation capabilities in order to create a dynamic multiple transmission network. Various relays are able to concurrently transmit information, taking into account the conditions of the wireless channel and the precise control of the signal phase, in such a way that all the signals are combined at the destination. For example, ideally, N transmitters send the same messages with the same power, while tolerating a path loss during the transmission of the signal to a normal destination increases the power at the destination by N times. This feature has been shown to increase base station anonymity in mobile sensor networks. This protocol appropriately disrupts the evidence hypothesis (EH) and it doesn’t consider the real base station of the mobile sensor network well.
This protocol is an effective technique for enhancing the probability of low-cost, multi-hop paths usage, and the power needed to transmit the signal to the destination is used as the cost of the L-link. Because, the average energy consumption cost of the protocol increases with increasing the number of auxiliary relays |L|, the use of L link selecting the paths to maximize |L| can increase the base station anonymity with energy costs equal to the mobile sensor networks with anonymous protection [11].
As far as we know, participatory communication was first used to reinforce the base station anonymity in Ref. [12]. As a result, the former studies of distributed beam formation and base station anonymity will be discussed separately. Researchers on the subject of base station anonymity initially defined a quantitative way of measuring anonymity. Some researchers developed sub-optimal effective approaches for measuring anonymity in the connection entropy [13], GSAT test [14], and belief [15] [16]. Entropy and GSAT methods impose certain limitations on the enemy. They give the a priori possibility that the location of the base station is known to the enemy or that the enemy can estimate the location of the base station. The functionality of the belief index, according to the evidence theory, does not have any of these hypotheses and so it has attracted a lot of attention as a metric for recognizing anonymity. In Section 4, we discuss the evidence theory and the metric of belief to evaluate base station security. Many published techniques for dealing with the traffic insecurity in mobile sensor networks applied various approaches to make the location of data sources hidden [17] [18]. Such as [19] in which the authors propose various approaches such as uniform packet speed and false paths to confuse the enemy. Similarly, the authors in [20] suggest that network paths be modified by considering virtual sinks. Two techniques have been proposed in [21]. In the first technique, the base station re-transmits a package of received packets at various degrees and the base station looks like an ordinary node for the enemy. The base station can also be considered and can move to a safer location.
The above techniques are used in the network layer of communication protocols. The protocol uses distributed beamforming in the physical layer to improve the base station anonymity [22]. This paper compensates for this shortcoming of the previous protocol by providing a multi-layered routing algorithm considering data link constraints and auxiliary intermediate nodes in order to decrease the total power utilization.
The authors in [23] proposed an efficient smart control plan for the dynamic transmission in the wireless sensor networks using cooperative protocols. This algorithm supports the dynamic operations of block data and third-party public validation to provide high security against data forgery and replacement. In [24], a QoS model for resource allocation algorithm was proposed for data replicas based on the servers existing in a network in order to improve the connectivity approach service and decrease total cost. In [25], a distributed algorithm was proposed to reduce the access delay and expand the network bandwidth. In this scheme, a new data analysis strategy was proposed to mitigate the costs of data storage and information transfer for applications. In [26], a close-loop content-oriented scheme was reviewed achieving higher performance for data-intensive applications. Also, some researchers addressed main critical challenges of this criterion, such as energy efficiency [27] availability [28], and security [29] of data access. However, heterogeneous MSN has security challenges, including vulnerability for sensors and association acknowledgment, that delay the rapid adoption of computing models.
Unfortunately, the abovementioned works cannot be considered as a proper approach for large-sized networks due to reliability conditions and high computational complexity at the central unit that significantly increases to the number of sensors in the network.
In this paper, we present the cost of the L link, which has been optimized for multi-hop paths that minimize the average power consumption of the mobile sensor network. Using simulations, we show that the cost of our link is such that it maintains the anonymity of the base station while reducing the communication energy consumption. This article continues as follows: Section 2 examines the system model and the problem formulation. Section 3 provides a framework for Distributed beamforming and the energy efficiency of the protocol. Section 4 describes the proposed approach in Power Optimization in mobile sensor networks. The numerical results and discussion of the proposed approach are presented in Section 5. Finally, Section 6 draws the conclusion and highlights future challenges to motivate the effective integration of beamforming-based mobile sensor networks with the diagnosis.
2- System Model and Problem Formulation
A- Network Model
In this paper, we consider a homogeneous model for a mobile sensor network in which all sensor nodes have the same capabilities in terms of battery life, type of radio communication, and network protocols. In this paper the sensors were considered as mobile nodes. The base station acts as a well for all data traffic generated by the sensor nodes. There is only one base station on the network. Our hypothesis is that the sensors can be aware about the locations of the base station and the neighbor sensors as well [30]. In addition, the cells are well-informed about the level of transmission energy needed to get all subsequent hops. Multi hop paths are followed to deliver data frames to the base station. Also, the cross-wave propagation model has been considered in this paper.
We assume that precautions are used in the design and operation of the base station to prevent enemy infiltration. For example, the base station maintains the transmission power level equal to the other cells (for example, updated path exploration and authentication messages) so that it cannot be detected from other sensor nodes by radio frequency analysis. Messages are transmitted with the header and encrypted message body. We assume that the TDMA Media Access Control Protocol (MAC) operates by synchronizing sufficient time on all wireless network sensors at tolerable shielding intervals [31]. All nodes in mobile sensor networks are considered as auxiliary relay options.
B- Problem Formulation
Assume that the mobile sensor network transmits the target sensitive data, which is a desirable target for the enemy. After identifying and abusing the base station, the enemy aims to carry out a DoS attack against the base station at any cost, such as physically destroying the base station. Also, the enemy is actively engaged in eavesdropping by being present in all parts of the mobile sensor network [32] [33]. The enemy is able to identify the location of all radio communications at the location of the network [34]. While the enemy monitors the traffic, we assume that the cryptographic system is robust enough so that the enemy cannot use the cryptographic system analysis to retrieve the contents of the body or header. The enemy uses the evidence theory traffic analysis to localize the base station, unaware that the mobile sensor network is using the distributed beamforming.
The enemy starts via monitoring the transmit links demonstrated by 
in which U is a direct connection among the nodes (
& 
). It also obtains the paths by correlating all the evidence for the node pair. The overall path containing two or more nodes is denoted by V, and the associated evidence 
is calculated as follows:
(1)
Normalized evidence 
shows a proportion of all the evidence gathered by the enemy that supports the 
which indicates the enemy's certainty that there is a path of length n in any given node and is expressed as follows:
(2)
In this paper, the Belief index has been applied in order to evaluate base station anonymity. The small belief metric means less confidence of the enemy or more anonymity of the base station coordination. To reduce the computational complexity of the calculations needed, it’s supposed that the enemy splits the mobile sensor network into an 
network consisting of 
square cells. This means that the enemy only needs to identify the target location within the cell. As a result, the belief metrics generated by cell analysis cause u to indicate that the sensor is not a specific sensor, but one of the cells in the enemy's target network. Section 5.b has presented an example of evidence theory analysis. Figure 1 illustrates the network configuration and the communication beamforming links between the sensors and the base station.
Fig. 1. The network configuration and the beamforming between sensors and the base station.
3- Distributed Beamforming Model
A. Distributed Beamforming
Based on the proposed methodology, a distributed beamforming approach has been applied to further identify the base station. Distributed beamforming uses the broadcast nature of wireless transmission. Adjacent nodes may also hear all the frames sent to a particular receiver. According to Figure 2, these adjacent nodes may act as auxiliary relays in cooperation with the transmission source so that the transmitted signal travels to through a diverse set of transmitters. Because each 
relay sends the same message with the exact time and synchronization of the carrier, the signals at the destination are combined under the conditions of ideal scheduling and carrier synchronization [35].
Fig. 1. Distributed beamforming model
Ideal received signal in by source and 
auxiliary relay is sent as shown below:
In this equation, h indicates the channel shock response, 
indicates the sharing efficiency 
is the carrier frequency, 
illustrates the phase modulation expression, 
is total phase variation term and 
represents the thermal noise contained in the receiver. 
consists of two expressions: Information received from source (i.e., 
) and total information received from the relay used 
is equal to 
.
The meaning of Equation 3 in terms of achieving base station anonymity in the physical layer is that the distribution of the distributed beamforming makes it possible for the component of the signal received from the transmitted information decreases by at the same time, the power level of the signal received by remains constant during the phase offset . Therefore, if and 
transmit only data at a specified SINR with the power required to reach , each transmitter can reduce the resource via 
dB and properly prevents the enemy from distinguishing 
. Accordingly, the elimination of from the enemy evidence set increases the confidentiality of the base station, as it reduces its role in detecting 
.
Figure 2 shows the functionality of the distributed beamforming used in any relay via the node , which intends to apply dynamic beamforming to increase the base station anonymity while sending a packet to the next relay node . should first choose the appropriate subset of auxiliary nodes by handshaking (according to steps (a) & (b)). When a vector of auxiliary relays is used, transfers the packet body in step d to . In step e, the distributed beamforming is submitted and then the authentication message is transmitted from node to in stage (f) to confirm the correct reception of the cooperative message.
B. Distributed Beamforming Protocol
Figure 3 is an example of an enemy theory (ET) analysis with and without using distributed beamforming for seven network sensors. In this example, the enemy divides the target area into 
cells. In Figure 3, the transmission of information in the main method along with the cooperative transmission by the distributed beamforming. In continuation, the paper shows the evidence collected and the belief calculation in unit hops in the main method and the distributed beamforming protocol, in which the relay in cluster 28 sends the packet to the relay in cluster 35.
Fig. 3. Applying enemy-theory for a mobile sensor network
C- Distributed Beamforming Energy Analysis
In addition to the higher anonymity of the base station, the energy cost of communications resulting from the use of the distributed beamforming protocol should also be considered. While it may save up to 
on the transmission power, additional signaling in the distributed beamforming protocol causes increase of the data overhead. Dedicated power for each sent bit 
can be computed by ratio of the mean transmit energy 
needed to reach the to information by the signal to noise ratio (SNR) and the set speed r, where 
. Therefore, the mean communication energy consumed by the non-cooperative system is equal to 
, where 
is equal to the average body size. Accordingly, the mean distributed beamforming power is calculated based on a cooperative hop and is obtained based on the following formula:
Where 
represents the Relay Request, 
denotes the Data Multihop and 
is equal to the header size of acknowledgement/negative acknowledgement.
In this scenario, the channel was considered to be equal to K bits at time t microseconds (i.e., 
) [7].
In equation (4), we have two key observations of the average the distributed beamforming energy consumption per hop (
). First, does not make saving on the distributed beamforming for power transmission (
) which involves the use of relays. As a result, uses relays that are as close as possible to , thus minimizing (
). Second, seeks to maximize by applying the maximum number of auxiliary relays possible and at the same time minimizing the use of transmission power ().
D- Selection of Relay in the Distributed Beamforming Protocol
The distributed beamforming protocol requires an approach to select a relay in order to use a set of in each hop and has three objectives: higher anonymity of the base station, conservation or reduction of communication energy compared to the main system 
without distributed beamforming and achieving the best CSI measurement. Increased | L | causes the reduction of the ability to communicate directly with and and thus improves the base station anonymity. But as we said before in the previous section, the increase of requires higher transmission power () to be used during relay operation. By iteration, we obtain the expected number of potential relays that made available by increasing the power level (). Given the constraint 
, we maintain the anonymity of the base station to prevent the enemy from collecting the evidence of linking the transmitter and receiver.
First, the quantity of the potential intermediate nodes was considered equal to 
where λ is the density of the node in the region, which is 
in a semicircle with radius 
is calculated based on the number of expected nodes in the receiving interval when is transmitted with power 
. This algorithm adjusts 
using δ through iteration where δ> 1, because δ = 1 means that 
and there is an undesirable link between and .
Our relay selection algorithm is briefly performed based on the below stages:
1- chooses the primary amount of δ and mathematically calculate 
. In the beginning, the set of the distributed beamforming relays is 
.
2- sends the relay request message and is calculated based on the δ to reach .
3- Nodes that respond to with a confirmation message involves the number of available relays 
| in a way that 
.
4. One of the following three results occurs for 
:
A- If 
, then
is used to increase and reach more candidate relays in each iteration. Return to step 2. If 
, the algorithm ends with and the distributed beamforming is not used in this hop.
B- If 
, then 
and the algorithm terminates.
C- If 
, L becomes of the relay of the highest quality which are prioritized based on the best condition of channel state information.
The proposed node selection approach is used at all relays next to the transmitter path to the base station. Decreasing δ is the only node solution to use more intermediate nodes to enhance 
. Nevertheless, the multi-layer routing approach offers another option to increase | L | for the mobile sensor network: In this case, the paths are selected as a functionality of the accessible auxiliary intermediate nodes in all hops.
The cost of a 
link consists of two parameters, each with a unique objective for the distributed beamforming. First, energy saving is the most important factor in the initial design of mobile sensor networks and is one of the fundamental technical design constraints for further anonymity of the base station. Instantaneous energy required to use a set of the auxiliary relay is equal to:
The reader is reminded that while selecting a relay, the energy to bit 
required to use the relay is dependent on δ. quantity of re-transfers needed for successful use of a suitable set of of the relay, a relay in which the relay selection condition is met is represented by n. Secondly, the average energy consumption of the distributed beamforming decreases with increasing the number of of the auxiliary relays available in each hop. As a result, the cost of the beam links is calculated based on the following formula:
When the auxiliary relays are available, the cost of link is decimal and when the relays are not available it becomes ∞. The cross-layer scheme integrates with the distributed beamforming relay selection algorithm so that each node can calculate and make better use of the distributed beamforming by selecting routes with higher relay densities.
4- Power Optimization in Mobile Sensor Network
According to the main sources in this field, there is no standard model for energy consumption in beamforming-based mobile sensor backhaul networks. However, the application of nonlinear prediction energy consumption in such systems has attracted more satisfaction. Here, this paper uses adaptive resource allocation in which the backhaul connection has been modeled, in which C5 and C6 are the maximum transmission power constraints for sensors and macro base stations, respectively.
A. Content-Caching Model
In this network, we suppose that content can be modelled as a distinct set of packet data as 
which 
represents the 
-th data frame. The request probability for data frame is expressed as
It should be noted that the caching model presented for this paper is stochastic caching so that we can calculate the probability of caching data packet f via base station i 
where illustrates the cache size. In addition, {
of base station 
should satisfy below condition:
means the cell is macro cell, otherwise, 
.
B. Resource Control Model
Based on the approach’s principles, the resources of the base stations can be supplied by conventional smart grid and renewable energy harvesting. During this scenario, the transmission power relevant to base station can be demonstrated by 
, and the applied energy from the grid network is illustrated by 
. The harvested renewable resource is shown as 
. Based on the enabled power sharing capability, the shared power among cell and cell 
is equal to 
, where 
denotes the power-sharing index among base stations. So, we can conclude that 
is equal to the loss percentage in the power sharing stage. The following condition should be satisfied during the power sharing process.
According to the defined conditions, the overall power efficiency can be affected by the transmission strategies, power sharing and the level of harvested energy from renewable resources.
C. Transmission Model
Taking fairness into account, we tried to provide data rate balancing throughout the network. In which, 
denotes the association indicator, for example, 
represents that node 
is associated with BS and otherwise the node has not been associated with the base station. Subsequently, 
represents the number of sensors associated to cell . 
express the probability of serving 
associated nodes by base station i. if , we can calculate the efficiency of the -th sensors as 
which 
is the throughput so that the is obtainable as.
In this framework, the ratio of signal to interference-noise can be computed via (11)
In this formulation, 
and 
indicate the main channel gain and the interfering channel gain respectively, B denotes the frequency bandwidth. 
is also a noise figure. We can consider the goal function equivalent to minimization of the applied grid power. Consequently, we have the goal function as the following.
P1: |
|
s. t. |
|
P2: |
|
s.t | C1 , C2 , C3 , C4 , C5, C6, C7, C8, |
where, 
D. Data Caching-based User Association Algorithm
In this framework, P2 as a NL mixed integer programming problem is not a convex problem and as Lemma 1 indicated, the sub gradient method will be the best approach to solve it. Taking 
into account, the sensor association problem will be mathematically modeled as follows.
P2.1:
|
s. t. C1, C2, C4, C5, C6, C9 |
|
|
s. t. | C1, C2, C6, C9. |
Parameter | Value |
Configuration of the Network | Mobile Network, X-sectored BSs |
sensor distribution model | uniform (U) and hotspot (Hs), |
transmit backoff | 1.5 dB |
Base Station MTP | 43 dBm |
Codec strategy | Adaptive multi-rate |
| 3 dB |
Propagation model | Okumura-Hata |
Fairness Index | Security/ Throughput |
Upper bound of iteration | 2000 |
| 5 dBm |
Learning factor | 1.1 |
Weighting factor | 0.77 |
Weighting factor | 0.28 |
MAX Sensor Power | 90 w |
MIN Sensor Power | 5 w |
Sensor transmit range | 30 m |
