Security and Communication Networks

Volume 2018, Article ID 7670939, 11 pages

https://doi.org/10.1155/2018/7670939

## Jammer Localization in Multihop Wireless Networks Based on Gravitational Search

^{1}Graduate School, PLA Army Engineering University, Nanjing 210007, China^{2}Nanjing Telecommunication Technology Research Institute, Nanjing 210007, China

Correspondence should be addressed to Jianhua Fan; moc.qq@372800732

Received 15 January 2018; Accepted 20 March 2018; Published 6 May 2018

Academic Editor: Ilsun You

Copyright © 2018 Tongxiang Wang et al. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.

#### Abstract

Multihop Wireless Networks (MHWNs) can be easily attacked by the jammer for their shared nature and open access to the wireless medium. The jamming attack may prevent the normal communication through occupying the same wireless channel of legal nodes. It is critical to locate the jammer accurately, which may provide necessary message for the implementation of antijamming mechanisms. However, current range-free methods are sensitive to the distribution of nodes and parameters of the jammer. In order to improve the localization accuracy, this article proposes a jammer localization method based on Gravitational Search Algorithm (GSA), which is a heuristic optimization evolutionary algorithm based on Newton’s law of universal gravitation and mass interactions. At first, the initial particles are selected randomly from the jammed area. Then, the fitness function is designed based on range-free method. At each iteration, the mass and position of the particles are updated. Finally, the position of particle with the maximum mass is considered as the estimated jammer’s position. A series of simulations are conducted to evaluate our proposed algorithms and the simulation results show that the GSA-based localization algorithm outperforms many state-of-the-art algorithms.

#### 1. Introduction

Multihop Wireless Networks (MHWNs) face various security problems due to their shared nature and open access to wireless mediums. Among all the security threats to the MHWNs, one typical case of attacks is jamming attack, which usually emits useless radio signal to disrupt normal communications between wireless devices by occupying the wireless channel or destroying the coupling of protocols with one or many low-end simple off-the-shelf wireless devices [1, 2]. For instance, different from interferences among wireless nodes [3], jamming attack can break down the MAC protocols by sending fabricated ACK or CTS packets to the wireless channel. Generally speaking, jamming attack can be initiated from different protocol layers and decreases the network performance significantly through limited resource consumption, which makes it be widely employed by adversaries.

In order to mitigate the impact of jamming attack and restore the normal communications, a series of anti-jamming countermeasures have been proposed from multiple network layers, such as channel-hopping, secure routing, and spatial retreat [4–7]. However, these anti-jamming strategies mainly provide useful approaches to avoid or evade an attack in order to maintain the normal operation of wireless networks. Although the negative impact of jamming attack can be mitigated, the networks can only adjust themselves passively without utilizing the information of jamming. Moreover, when conducting the anti-jamming measures, the constraints of wireless devices including limited memory and energy supply and low computation capabilities must be considered.

Besides these passive anti-jamming measures, another way is to locate the jammer and obtain the position information of jammers, which makes it possible to eliminate the jammer from the networks by physical methods or manual ways. Actually, the position information of jammers may allow better deployment of wireless devices and provide useful information when designing MAC or routing protocols.

Up to now, jammer localization has been widely investigated and a number of localization algorithms have been proposed. In conclusion, exiting jammer localization algorithms can be divided into range-based methods and range-free ones. Range-based algorithms need to estimate the parameters of wireless channel in advance and calculate the relative distance between nodes and the jammer. Although some typical models of wireless channel have been proposed, the parameters of wireless channel can be hardly estimated accurately in real scenario. Besides, the performance of range-free algorithms can be easily affected by the distribution of nodes and the jammer’s parameters.

In order to reduce the sensitivity of range-free algorithms and improve the localization accuracy, a robust jammer localization algorithm based on Gravitational Search Algorithm (GSA) is proposed in this paper. At first, several related models, that is, network model, jamming model, and communication model, are illustrated. Then, the GSA-based jammer localization is presented, which mainly consists of selection of initial particles, determination of fitness function, resultant force calculation, and parameters update. At last, a series of simulations are conducted to evaluate the performance of our proposed algorithm. Compared with many state-of-the-art jammer localization algorithms, our algorithm performs better in many different scenarios with different parameter settings.

The architecture of this article is organized as follows. Section 2 summarizes related work. Several related models are introduced in Section 3. Section 4 presents our jammer localization strategy based on GSA in detail. Simulation experiments and results are described in Section 5. The main work is concluded in the last section and some discussions on the future work are highlighted.

#### 2. Related Work

Over the past few years, Xu et al. conducted a series of researches on the jamming attack and four basic approaches of jamming attack were proposed [8], which were defined as constant jamming, random jamming, proactive jamming, and reactive jamming. Wei et al. provided a comprehensive survey of the major works done in the field of jammer localization for MHWN [9].

Range-free localization algorithms utilize the geometric knowledge of the jammed area to locate the jammer. Wang and Zheng took the weighted factor determined by the relative position between jammer and node into consideration when modifying the Centroid Localization (CL) [10] and presented Weighted Centroid Localization (WCL). Liu et al. put forward the Virtual Force Iterative Localization (VFIL) to locate the jammer [11]. At first, the jammed area and jamming range were estimated by VFIL. Then, the estimated position of the jammer was amended iteratively in order to cover the most jammed nodes. Sun et al. computed the convex hull that was determined by the boundary nodes to locate the jammer [12]. The minimum circumscribed circle was achieved based on the convex hull and the center of it is the estimated jammer’s location. Similarly, -hull was adopted by Zhang et al. to obtain circumcircle of the jammed area and then the least square circle was formulated to estimate jammer’s location [13]. In addition, Wei et al. also made the research on the collaborative mobile jammer tracking in MHWN and the jammer is located based on multilateral localization method [14]. For multi-jammers scenario, Cheng et al. put forward the M-clusters and X-ray to estimate jammers’ positions, respectively [15]. Wang et al. proposed the* k*-mean cluster algorithm based on the neighbor nodes’ information to estimate the positions of the jammers [16].

The relationship between jammer and node is established based on wireless channel model to locate the jammer for range-based localization algorithms. Pelechrinis et al. pointed out that Packet Delivery Rate (PDR) decreased with increasing distance between node and the jammer [17]. So value of PDR could be used to indicate the influence that the jammer had on the node. They proposed a light distributed jammer localization algorithm based on PDR and the node would select the node with minimum PDR from its neighbor nodes as the next hop. Liu et al. proposed the jammer localization algorithm based on the nodes’ hearing range [18], which is defined as the maximum distance for the node that can successfully decode the signal generated from other nodes. Wang et al. put forward the scheme to locate the jammer based on the combination of PDR gradient descent and power adaptive technique [19]. The power would increase at the termination node of PDR gradient descent and the localization accuracy was improved a lot compared to that of PDR gradient descent.

#### 3. System Models and Problem Formulation

This section analyzes the impact of jamming on the legal communication link and introduces several related models. The nodes in the jammed network can be divided into three categories based on the impact of jamming, that is, unaffected nodes, boundary nodes, and jammed nodes.

##### 3.1. Impact of Jamming

According to the characteristic of wireless communication, the signals cannot be decoded correctly if the received SNR is lower than a certain threshold. Assume that the interference among nodes has been avoided through specific MAC or network protocols, such as TDMA and 802.11 DCF. Thus, the overall interference mainly includes the background noise for nonjamming scenes and the background noise and jamming signal for jamming scenes. For the transmitter and receiver* j*, the received of node iswhere represents the jammer and is the received jamming power at node* j*. and represent the received power of node and the power of background noise, respectively. The state of link between node and node is defined as :where is the received SNR threshold for all the nodes. denotes the normal communication between node and node . The communication links among nodes are bidirectional and the link between node and node is considered to be connected when both the conditions and are satisfied.

##### 3.2. Related Models

###### 3.2.1. Network Model

The characteristics of MHWN model considered in this article mainly include the following.

*Multihop and Stationary*. Once deployed, the position of MHWN node remains unchanged and the nodes communicate with each other through multihop fashion. The nodes are assumed to be time-synchronous, which can be achieved by the clock calibration after initial deployment.

*Location-Aware*. The MHWN nodes can be aware of their own locations and their neighbors’ locations through GPS or specific location-aware algorithms and many applications also require the location of nodes in order to provide specific services. Assume that the locations of nodes have been obtained after initial deployment.

*Neighbor-Aware*. Each node can store its neighbors’ information and update a neighbor list at regular intervals. The list can be achieved by several routing protocols, such as AODV and DSR.

Besides, each node is equipped with omnidirectional antenna and transmits signals with the same power. In other words, the nodes are homogeneous in MHWN.

###### 3.2.2. Jamming Model

The jammer considered in this article remains static and the jamming power remains unchanged. Besides, the constant jammer equipped with omnidirectional antenna is adopted in this article, which transmits RF signals consistently.

###### 3.2.3. Node Model

The nodes deployed in the MHWN randomly can be divided into jammed ones, boundary nodes, and unaffected ones according to different degree of jamming produced by the jammer:(i)Unaffected node: a node is determined to be unaffected if it can receive packets from all of its neighbors after the appearance of the jammer(ii)Boundary node: the node is considered as a boundary node if it loses some of its neighbors, while it can still communicate with part of the unaffected nodes(iii)Jammed node: the jammed node is defined as the node that cannot receive any message from all the unaffected nodes and boundary nodes

###### 3.2.4. Wireless Channel Model

Typical wireless channel models mainly include free-space propagation model, shadow-fading model, and exponential-fading model [20–22]. The shadow-fading model is adopted here to model the small-scale fading circumstance. If the receiver locates at the distance from the transmitter, the received power iswhere represents the received power at specific distance and is the fading index. is the Gauss random variable with zero mean and variance .

##### 3.3. Problem Formulation

A typical jammed network scenario is illustrated in Figure 1. We aim at locating the jammer under the above settings by using the jamming information. To achieve this goal, several challenges need be solved and we present our basic ideas here. At first, each node should determine its state based on the neighbors number, received SNR, and so forth. Then, we need to decide the jamming information that could be collected by wireless nodes, such as sensor node. Besides, the information can be used to detect the jammer’s existence. At last, an efficient localization algorithm needs to be carefully designed considering both the complexity and accuracy.