Abstract

Keywords

References

Abstract

At present, wireless sensor networks are developing rapidly with the support of the Internet of things. Wireless sensor networks can deliver the information people need at any time, free from the constraints of time and space. Wireless sensor network is widely used, which lays a solid foundation for the development of Internet of things. As the node deployment environment of wireless sensor networks is usually very complex, it is necessary to study the security of wireless sensor networks so as to reduce security threats and network attacks. In this paper, the security and application of the wireless sensor network are emphasized.

1. Introduction

Wireless Sensor Network (WSN) is perception, and the main tool is sensor. The use of sensors can effectively sense the external environment, and then with the help of wireless network for information transmission, to meet user needs. The security of wireless sensor network must be paid attention to, because it is supported by high technical content and complex structure, once there is a problem, the consequences are unimaginable. The development of the Internet of things is inseparable from wireless sensor networks, and the ability of people to communicate quickly is also closely related to it [1].

WSN can be used to collect all kinds of data and information and deal with all kinds of complex environments, the application of WSN covers all aspects. In the military field, it can be used to detect the deployment of troops in enemy terrain and other situations, and it has the function of detecting biological and chemical pollution and nuclear radiation. In terms of environmental monitoring and protection, it can be used for data collection in the field environment, tracking animal footprints, analyzing pollution situation, and predicting the explosion of forest fire and debris flow. In the field of industry and agriculture, the monitoring of crop growth and product flow intelligent production. In the field of medical care and health, it can realize the function of obtaining physiological data of patients, analyzing their conditions, and timely receiving medical treatment. In addition, wireless sensor network plays a crucial role in the field of smart home, smart transportation, smart city, cultural relic protection and business, etc. Wireless sensor network is changing people’s life [2].

The information security of WSN is concerned in many contexts, such as military medical disaster prevention and other fields. Wireless sensor networks use open wireless communication channel technology to transmit data, but without security protection means, data is very vulnerable to internal and external attacks [3]. However, the amount of computation based on the cryptographic defence method is not suitable for WSN network. Therefore, it is also a big problem to choose an appropriate encryption method to ensure the security of WSN information [4] .

A wireless sensor network (WSN) is deployed in an open and sensitive environment like health monitoring, military surveillance, industrial monitoring, landslide detection and so on. It consists mainly of two types of nodes: sensor nodes and sink nodes. The sensor nodes are similar to that of a small computer, but they have limited processing capability, memory and battery power. Due to the resource constraint nature of sensor nodes, the direct transmission of raw data from the sensor nodes to the sink nodes consumes more energy and leads to a lot of congestion in the network. To reduce energy consumption, data aggregation is introduced in a WSN, as shown in Figure 1. More powerful sensor nodes act like aggregators (cluster head) or, in some cases, regular nodes act as aggregators (Castelluccia et al., 2009). These aggregators collect and process the data coming from the other sensor nodes of the network. For example, the aggregation functions like sum, min and max are useful in calculating temperature readings. The aggregator collects the data from n sensor nodes, calculates the sum of all n values and forwards it to the sink node. The sink node, after receiving the sum value, calculates the average temperature by dividing the value by n. The data aggregation saves the energy of sensor nodes and, thereby, increases the lifetime of the network (Papadopouloset al., 2012). Security of data aggregation is a challenging task in WSNs. False aggregation may lead to a large damage in the sensor network. For example, in the case of health-related applications, the attacks on sensor nodes may affect the person’s life, and in the case of military applications, it may lead to a national security threat. Various attacks like node compromise attack, eavesdropping, collision attack, Sybil attack, blackhole attack, sinkhole attack, selective message forwarding and attacks on data aggregation occur. (Xing et al., 2005). So, the secure data aggregation needs the following key properties: confidentiality, integrity, authentication and freshness of data (Papadopouloset al., 2012). The objective of the present study on Secured Data Aggregation (SDA) in WSNs is to securely transmit the aggregated data to the base station by reducing the energy consumption of sensor networks. The Paillier additive homomorphic cryptosystem is used to encrypt the data, and Boneh et al.’s aggregate signature method is used to generate the signature at each sensor node (Boneh et al., 2003).

I- Introduction

II- LTE physical layer

III- Downlink physical layer decoding protocol

IV- Types of jamming

V- Jamming vulnerability analysis of LTE physical layer

VI- Test setup for jamming margin measurement

VII- Mitigation techniques

VIII- Conclusion

References

Abstract

In Wireless/mobile communication, the requirement of higher throughput and data rates are increasing day-byday. For meeting this requirement, Long Term Evolution (LTE) technology has become the prime choice. Communication using wireless technology has vulnerabilities. LTE wireless technology has vulnerabilities associated with the processing technique as well as well-defined positioning of channels and signals of physical layer. This drawback is of concern, as it can completely or partially block the communication, due to intentional jamming or unintentional interference. To understand the jamming vulnerabilities in LTE, it is essential to understand various physical channels, signals, frame structure, decoding protocols and various types of jamming. This paper provides jamming vulnerability analysis of LTE physical layer, physical channels and signals. Practical lab test setup for measuring jamming margins along with the result is provided in this paper. Various mitigation techniques have been provided in this paper to reduce jamming vulnerabilities.

Introduction

3rd Generation Partnership Project (3GPP) has standardized Long Term Evolution (LTE) technology to meet the demand of growing data rates and higher throughput for cellular communication. As compared to the existing cellular technology, LTE provides higher data rate, lower latency, higher spectral efficiency and higher capacity [1]. Hence, LTE has become the prime choice to meet the growing demand of high data traffic. LTE has already been proposed for public safety network and is in investigation for end use in military application fields. Wireless technologies are vulnerable to radio frequency interference and jamming, due to which the communication link may suffer partial or complete blocking. Unfortunately, wireless communication link undergoes unintentional interference and intentional jamming. Like any wireless technology, LTE is also vulnerable to disruption. Various types of attack possible on LTE are Denial of Service (DoS) and information extraction [2]. Typically, DoS is caused by jamming attacks due to which communication link gets disrupted. DoS at higher layers and attacks for extracting information, falls under cyber attack. In this paper, we have focused to assess the physical layer vulnerabilities and threats which can potentially cause DoS by intentional jamming. The objective of the paper is to provide the insight to the jamming vulnerabilities in LTE physical layer and the techniques to mitigate and withstand jamming.

Abstract

For about last two decades research and development processes in area of Electronics and Sensors have resulted as significant advancements in electronics and technology, small size, cost effectiveness have resulted sensor nodes as crucial part of real world problem solution applications. The wireless sensor nodes are spread over an area to record the facts of situations like fire, flood and then forward same to meaningful data to the a node, which is generally cluster head node for calculations, resulting an alert to take necessary measures to control the situation. In last few years, WSN have increased significantly in variety of areas and applications, resulted the contemporary demand of high, consistent security mechanism. Also, there is variety of attacks on WSN at their different layers. These tiny, low processing sensor nodes are not strong enough in terms of power, handling attack issues etc. On other hand, applications based on these sensors demand on-time streamed data or information is to be collected and then to send same on a reliable, secure delivery mechanism. Tiny sensors with limited hardware, processing are not able to afford old and in practice security protocols or algorithms to face or sustain the attacks. Many attacks impact WSNs. at their different layers an affect sensor’s roles like signaling, framing, transmission etc. Many attacks have been identified at each layer of WSN which are intended, pre-planned attacks to obstacle the availability of service, restricting the sensor node’s utilization in solution of a for problem. This paper is mainly focused on WSN structure, threats, attacks and security requirements and security measures against attacks; especially various types of attacks starting from physical layer and data link, network layers, subsequently and particularly variety of attacks at transport layer in details with some effective suggestions as prevention or protection against those attacks.

Introduction

As a result of recent developments in wireless technology polishing, wireless networks are now believed as a reliable architecture medium to deliver communication with major security parameters confidentiality, integrity and availability and non-repudiation. Wireless Sensor Networks consist of less power, less processing capability, small size nodes[1]. Hence, It becomes very tough to raise the capability level of such tiny sensors due to their various constraints. Constraints, associates with sensors are to be considered seriously while designing a secure real world problem solution using WSN.

The next generation of wireless access systems (5G) aims to improve the Quality of Experience (QoE) for critical services, as well as to provide high availability, low latency, elasticity, and increased security [1]. SDN, NFV and Network Slicing are new concepts and technologies that have emerged as 5G enablers. These technologies can be leveraged to provide Security as a Service (SECaaS) by deploying Security Virtualized Network Functions (VNFs) within different slices and ensuring optimal resource provisioning to reduce Operational Expenditures (OPEX) while ensuring the provisioning of the Service Level Agreement (SLA). Furthermore, proper resource allocation is crucial as a malfunctiong security VNF can compromise the network; therefore, a predictive auto-scaling function, implementing application-specific policies, needs to be deployed along with the monitoring and flow control mechanisms. Recent works have investigated solutions to enable autoscaling mechanisms [2], [3], factors that impact the scaling delay [4]–[6], and mechanisms relying on SDN to enable security functions [7], [8]. Other solutions have focused on the performance evaluation of security VNFs [9]–[13]. To the best knowledge of the authors, no prior work has been taking into account ways to enforce security within 5G slices. This paper proposes and evaluates an architecture that leverages both SDN/NFV capabilities to enable SECaaS in multi-cloud environments. The rest of this paper is organized as follows. Section II discusses previous works on the subject of this paper. Section III presents an overview of the proposed architecture for enabling SECaaS. Section IV provides the methodology for our experimental evaluation, and discusses the results. Finally, Section V concludes the paper.

Wireless sensor networks are a set of spatially distributed autonomous nodes over a large area that can cooperatively monitor and communicate the data to the central server through wireless network [1]. Sensor networks are being utilized in many areas, including environmental conditions monitoring, military application, tracking and monitoring animals, detection of harmful animals, smart buildings, structural health monitoring, automobiles, and so on. These wireless sensor networks are capable of detecting specific conditions, such as temperature, pressure, humidity, sound, motion of objects, pollutants. In every application, security is thefundamentalfactor, and mechanisms must be designed to have a secured environment.

1.1. Motivation

The survey in this context summarizes the application scenarios of wireless sensor networks. Here, we focus on three application scenarios – environmental wireless sensor networks, structural health monitoring, and smart building wireless sensor networks. And these three application areas are scaled to large-, medium-, and small-scale applications, respectively. The below Figure 1 shows the scheme of study. We summarize and compare pros and cons for each application. Based on this analysis and comparison summarized, we briefly investigate the open issues. The investigations raise potential research questions and hence, it gave us strong motive to conduct broad research in this area.

1.2. Application scenarios

Wireless sensor network applications handle the sensitive data and important information, hence the wireless sensor networks are considered as critical systems. The wireless sensor networks are widely used to communicate and process data among the nodes. Over the last decade, the evolution of sensor network technology is at its peak where the applications developed focusing various operational areas. For the practical research ideology, the real-world scenarios are studied under three applications.

1.2.1. Environmental wireless sensor networks

Environmental wireless sensor networks is a broad research area and it is observed that environmental sensor networks as a large application has rapid development in recent years. In this applications category, spring brook rain forest which is one of the world heritage in Australia is considered as one of the motivating application and the security issues and vulnerabilities are studied in this context [2,3]. The spring brook rain forest is the long term and the large-scale monitoring wireless sensor networks. The wireless sensor networks of spring brook rain forest is the means for tracking the restoration of bio-diversity. The major responsibilities of wireless sensor networks on spring brook rain forest are temperature monitor, wetness of the leaves, speed of the wind, soil moisture, and direction of wind. It also includes the video monitoring along with the bio-acoustic technique [4–6].

Over last few decades, the popularity of a topic, which is more prone to be important for civil and military applications are increasing. Several applications of WSNs are emergency scenarios, manufacturing environments, battlefields, etc. Due to the advancements in fields, like microelectronics, integrated electronics, the development of sensor nodes is intensified [1]. The properties that combined in networks are mobile, sensors, wireless, and ad hoc. These properties are implemented in the real world for energy emergency response information [2], and monitoring factory environments [3]. The networks are vulnerable to various kinds of security threats from intruders at the network layer [4]. The main motivation is that the sensor nodes form a structure to observe the actions in unpredictable environments by acting in a selfcompositely, self-harmonize ad hoc manner, i.e., without the need of human intervention [1]. Routing is the process of selecting the most efficient paths in a network. The router presents the direction of traffic activated on the web [5]. The routing of data packets from a source to a sink through the network is of a more interesting challenge for the researchers in the WSN domain. Limitation of energy resources is a major impact, as energy is an important and fundamental component in designing of the routing protocols [1]. Routing is operated for various forms of sensor networks that consist of electronic information sensor networks, transportation networks, and the public switched telephone network. The main requirement of WSN is the trust on the behavior [6]. The data packets can be routed in the largest routing network around eloquent areas so that a complete mishap of the network can be overlooked [1]. The secure routing protocols [7] are not effectively averting malicious nodes that are authorized to the sensor network from doing any illegal activities [8]. In extension with, to dodge a strain of certain nodes to curtail the jeopardy barrier of the network, the routing algorithm should haul load balancing into rationale, directing to misplaced paths between the source and the sink. Furthermore, to lessen superfluous transmissions of the same data, the coalition of sensed data needs to be considered in WSN routing protocols [1].

As interest in ubiquitous environment construction is growing, Radio Frequency Identification (RFID) technique and Wireless Sensor Networks (WSN) [28] are also attracting a lot of attention as core technologies. With the rapid expansion of the ubiquitous environment, in the near future WSNs will be a familiar and vital element in our daily lives. Under the humancentric ubiquitous environments WSNs will collect and transmit a variety of personal data such as our tastes, living patterns, experiences and so on. Therefore, to protect the private data of users, security requirements for WSNs are assuming more importance and the need for review and study of the security of the WSN system will be critical factors in developing WSN applications. Moreover, multimedia data has always huge size and relate with lots of information around us, so it is very important to consider the multimedia security requirements in WSN system. A WSN is a collection of nodes equipped with processing capability such as one or more microcontrollers, CPUs or DSP chips organized into a cooperative network [12]. Each node may contain multiple types of memory for program, data, a RF transceiver usually with a single omni directional antenna, a power source, and accommodate various sensors and actuators. After being deployed, these nodes communicate wireless method and organize in an ad hoc network [29]. As their applications increase in a variety of fields, these systems are revolutionizing our daily life and workspaces. WSNs have already been applied to a variety of fields in our living environment. For example,an environmental monitoring system monitors air, soil and water and perform condition-based maintenance and a habitat monitoring system tracks plant and animal species population and behavior. Also, WSNs have been applied to seismic detection, military surveillance, inventory tracking, smart spaces etc. In fact, due to the pervasive nature of micro-sensors, sensor networks have the ability to change the way I construct complex physical system [34]. Now, as WSNs have become a fundamental element in the humancentric ubiquitous environment, the number and variety of applications will grow. With the rapid growth of WSN applications, the need for security for WSNs becomes vital. However, WSNs must overcome a variety of constraints such as limitations in energy consumption, processing capability, and storage capacity, etc. Many innovative security protocols and techniques have been developed to solve these limitations. Among the many ways to provide security [14], one studied most intensively is cryptography. Deciding the appropriate cryptography method for sensor nodes is fundamental to providing security services in WSNs [26]. The studies about cryptography techniques are useful to protect the data transmitted between the sensor nodes. However, security of an overall software system handling and processing the data transferred and stored from the WSN system is also an important concern and must be considered in designing the WSN system. Because the cryptography techniques are appropriate for the security of the overall software system, suitable security attributes need to be considered and implemented. The data generated presently in WSNs is different from previous data processed by an existing database system. The size of the data may be small but the data is generated more rapidly and in huge amounts. Therefore, the security attributes and their relative priorities are different from general software systems.

The Internet of Things (IoT) has developed a lot in recent years [1-2], and the Social Internet of Things (SIoT), a new application of IoT, is now evolving. The SIoT is a larger social network, connecting people and people, people and objects, and objects and objects. Using the perceptual monitoring technology of IoT, every building, car, or shopping mall can post a message automatically, realizing the interaction of people and a specific object. One part of SIoT can be a wireless sensor network (WSN), sensing the state of an object or monitoring an event in the network. Since SIoT enables interaction of an object with people or another object, there will be wireless communication between objects and people. In that case, The use of wireless communication media means that anyone with powerful radio transceivers can attack the network. Because of this vulnerability, SIOT faces security threats such as information eavesdropping, data fabricating, node compromising, and route disrupting. These network attacks threaten either content privacy, the confidential data of a message, or contextual privacy, information about the surrounding network. All these problems make privacy protection of the SIoT becomes essential. Wireless sensor networks (WSNs) show great application value for national security, military monitoring, health care, and environmental monitoring [3-5]. However, wireless network privacy issues have become the main bottlenecks for its further development. In this paper, we focus on protecting source location in a WSN related to SIoT. Location privacy is a typical context privacy issue. Particularly, an adversary can detect the message flow to determine the source node location and then attack the target of interest. This problem of source location privacy (SLP) has not been addressed effectively because it cannot be easily solved by encryption or authentication. Wireless sensor nodes have limited storage space, energy supply, and computing capacity. In order to preserve the location of the sensitive source node, we must develop a feasible and energy-efficient protocol to hide the source from being detected by adversaries. To clearly illustrate the SLP issue, we consider the classical “panda-hunter” model in Figure 1. A WSN can be deployed in a wildlife habitat to monitor pandas’ habits and behavior. Once a node detects a panda in its communication range, it becomes a source node and periodically sends report packets to the base station (Sink in Figure 1) by multi-hop wireless communications. Obviously, this scenario is unsafe for the source since the communication signal is exposed to the air. The hunters and poachers (Adversary in Figure 1) can use expensive devices to eavesdrop on the network, following the flow of packets, speculating the routing pattern and finally tracing back hop-by-hop to find the panda. Thus, hiding the location of a source node is a critical issue for WSNs.

The Internet of Things (IoT) is a smart network which connects all things to the internet for the purpose of exchanging information with agreed protocols [1]. So, anyone can access anything, at any time and from anywhere [2]. In IoT network, things or objects are wirelessly connected with smart tiny sensors. IoT devices can interact with each other without human intervention [3]. IoT uses unique addressing schemes to interact with other objects/things and cooperate with objects to create new applications or services. IoT introduces various applications like smart homes, smart cities, health monitoring, smart environment, and smart water [4]. With the development of IoT applications, there are so many issues raised. Among many other iss ues, security issue of IoT cannot be ignored. IoT devices are accessed from anywhere via untrusted network like the internet so IoT networks are unprotected against a wide range of malicious attacks. If security issues are not addressed then the confidential information may be leaked at any time. Thus, the security problem must be addressed.

 Confidentiality: An attacker can easily intercept the message passing from sender to the receiver so that privacy can be leaked and content can be modified [5]. So that secure message passing is required in IoT.

 Integrity: The message must not be altered in transit; it should be received at receiver node same as it is sent at sender node. Integrity guarantees that message has not been altered by unauthorized p ersons while in transmission [5].

 Availability: Data or resources must be available when required [5]. Attackers can flood the bandwidth of resources to damage the availability. Availability can be damage by malicious attacks like Denial of service (DOS) attack, flooding attack, black hole attack, jamming attacks etc.

 Authenticity: Authenticity involves proof of identity [6]. Users should be able to identify each other’s identity with which they are interacting. It can be verified through authentication process so t he unauthorized entity cannot participate in the communication [7].

 Non-Repudiation: Non-repudiation ensures that the sender and receiver cannot deny having sent and received the message respectively [8].

 Data Freshness: Data must be recent whenever required. It guarantees that the no old messages replayed by an adversary [9].

due to the characteristics of wireless channels, it is particularly vulnerable for wireless networks to be attacked by an eavesdropper, which puts forward great challenges to secure communications. Traditional security approaches apply different cryptographic algorithm to achieving confidentiality and authentication in communications. Meanwhile, physical layer security (PLS) is applied by Shannon theory to exploring secrecy communication by coding. The basic idea of PLS is to explore the random nature of physical layer media to enhance the quality of legitimate channel while attenuating the quality of eavesdropping channel. Compared with traditional cryptographic technologies mainly at upper layers, PLS shows more security in some degree with its advantages, for example, applying proper coding and signal processing to guarantee message confidentiality, while traditional cryptographic methods can be broken by advanced computing technologies; PLS can be easily and conveniently achieved rather than massive resources and infrastructure exploited by sharing cryptographic materials among legitimate users; and the authentication of PLS for legitimate nodes is quick. Hence, as an important and promising technique to prevent eavesdropping, it has attracted more attention in both industry and academic fields [1-5]. Related works in physical layer security were pioneered by Shannon’s information theoretic secrecy analysis [1] and Wyner’s discrete memory-less wiretap channel secrecy analysis [2]. Wyner proved that if it is a degraded version of the main channel for the eavesdropper’s channel, the source and destination can achieve a positive perfect information rate, which is defined as the secrecy rate. And secrecy capacity is defined as the maximum secrecy rate from the source to the destination. In order to enhance physical layer security, there are numerous studies focused on node cooperation approaches, especially three-point cooperation mode with one source, one destination and one relay (or multiple relays) [3-5].