Secure and efficient wireless sensor networks

Wireless sensor network (WSN) finds vast real-world applications in the field of energy control, security, health care, defense, and environment monitoring. WSNs are subdued by limited power with a specific battery backup. Due to the large distance between sensor nodes and sink, more consumption of power takes place in the sensors. Limited energy of sensor nodes is a major drawback to empower a large network coverage area. Therefore, the battery life and location of cluster heads play an important role in increasing the efficiency and lifetime of sensor nodes for long-term operation in WSNs. While there are many algorithms leading to the optimization of performance using convergence, comparison of such algorithms and their advantages and challenges is addressed. Different types of attacks and security goals are described for high-level security and privacy in WSNs. This paper tabulates a systematic survey of the evolutionary algorithms of WSNs based on nature. This paper also intends to reflect on the security challenges of WSN and proposes effective techniques to address them.KeywordsGAPSOACOBBOGWOEnergy utilizationWireless sensor networkIoT

Being one of the main technologies for Internet of Things (IoT), wireless sensor network is absolutely critical and plays an increasingly important role in the development of Internet of Things. Wireless sensor network is mobile network composed of sensor nodes with limited resources, like computation capability, energy, memory storage, and communication capability. Its characteristic results in that the security of wireless sensor network faces a great challenge. Hence, the security of wireless sensor network is an important issue for the development of IoT and shall be focused on. In this paper, a novel authentication scheme is proposed for wireless sensor networks based on theory of quadratic residues. It is making the use of master key to achieve simple symmetric cryptographic primitives and authentication operation and reach the aims of great resistance against the attacks and low energy consumption. Then the proposed scheme is compared with other network-wide key management schemes, obtaining better results in the aspects of security, efficiency, and scalability.

A scheme for WSNs (wireless sensor networks) security is given by dividing sensing tetrahedron into clusters and using the symmetric polynomials in this paper.The sensing tetrahedron is divided into a number of small grids.All those sensor nodes, both ordinary sensor nodes and heterogeneous sensor nodes are distributed in the sensing tetrahedron.In a grid, all ordinary sensor nodes and heterogeneous sensor nodes establish their shared keys through using the symmetric polynomials.All the heterogeneous sensor nodes establish their shared keys through using the symmetric polynomials.At last, all sensor nodes establish their keys directly or indirectly in the whole sensing tetrahedron.Analysis and comparison demonstrate this scheme enhances the WSN security, has good network connectivity, saves node storage, reduces network computing load, and extends the network lifetime.

The resource-constrained nature of Wireless Sensor Networks (WSNs) makes efficient identification of rogue nodes a significant problem. A scalable, lightweight algorithm that can detect and mitigate harmful behavior is the goal of this effort to improve network security. The Rabin-Karp method, well-known for its pattern-matching efficiency, is modified to verify transmitted data packets using hashes. To guarantee the integrity of data flow inside the network, the technique uses hash comparisons to identify inconsistencies suggestive of rogue nodes. Maintaining high detection accuracy while reducing computing overhead, false positives, and energy consumption is the goal of the approach to be designed. To optimize network performance, the algorithm runs at the level of the cluster heads and filters packets before they reach the base station. The objective is to provide a dependable, scalable, and energy-efficient solution for WSN security. This will ensure that data remains intact and that rogue nodes cannot disrupt the network. This method improves the reliability of WSNs and guarantees continuous monitoring, making them ideal for mission-critical applications. Incorporating the Rabin-Karp algorithm solves the urgent problem of trustworthy malicious node identification in contemporary WSNs by striking a compromise between computational efficiency and effective security.

We investigate k-connectivity in secure wireless sensor networks under the random pairwise key predistribution scheme with unreliable links; a network is said to be k-connected if it remains connected despite the failure of any of its (k - 1) nodes or links. With wireless communication links modeled as independent on-off channels, this amounts to analyzing a random graph model formed by intersecting a random K-out graph and an Erdös-Rényi graph. We present conditions on how to scale the parameters of this intersection model so that the resulting graph is k-connected with probability approaching to one (resp. zero) as the number of nodes gets large. The resulting zero-one law is shown to improve and sharpen the previous result on the 1-connectivity of the same model. We also provide numerical results to support our analysis and show that even in the finite node regime, our results can provide useful guidelines for designing sensor networks that are secure and reliable.

With the deepening of the research and development in the field of embedded devices, the paradigm of the Internet of things (IoT) is gaining momentum. Its technology’s widespread applications increasing the number of connected devices constantly. IoT is built on sensor networks, which are enabling a new variety of solutions for applications in several fields (health, industry, defense, agrifood and agro sectors, etc.). Wireless communications are indispensable for taking full advantage of sensor networks but implies new requirements in the security and privacy of communications. Security in wireless sensor networks (WSNs) is a major challenge for extending IoT applications, in particular those related to the smart-agro. Moreover, limitations on processing capabilities of sensor nodes, and power consumption have made the encryption techniques devised for conventional networks not feasible. In such scenario, symmetric-key ciphers are preferred for key management in WSN; key distribution is therefore an issue. In this work, we provide a concrete implementation of a novel scalable group distributed key management method and a protocol for securing communications in IoT systems used in the smart agro sector, based on elliptic curve cryptography, to ensure that information exchange between layers of the IoT framework is not affected by sensor faults or intentional attacks. In this sense, each sensor node executes an initial key agreement, which is done through every member’s public information in just two rounds and uses some authenticating information that avoids external intrusions. Further rekeying operations require just a single message and provide backward and forward security.

Wireless sensor networks (WSN) include numerous sensor nodes deployed to collect data efficiently. Energy constraints and unreliable communication remain critical challenges in WSNs. This research proposes a Fuzzy Trust‐based Hybrid Levy Snake Optimization model to improve energy efficiency, scalability, and reliability in WSNs. The Fuzzy Trust‐based Hybrid Levy Snake Optimization model includes cluster formation, optimal cluster head selection using combined levy flight and snake optimization, and a fuzzy trust mechanism to enhance data security and minimize energy consumption. The algorithm identifies secure and energy‐efficient routing paths by evaluating trust factors and optimizing cluster head selection based on parameters like energy and delay. Extensive experiments validate the Fuzzy Trust‐based Hybrid Levy Snake Optimization model, demonstrating superior performance compared to traditional approaches. The Fuzzy Trust‐based Hybrid Levy Snake Optimization model achieves a packet delivery ratio of 98.5%, throughput of 98.7%, and network lifetime of 9500 ms while reducing energy consumption to 83 J and end‐to‐end delay to 0.01 ms. These findings highlight the Fuzzy Trust‐based Hybrid Levy Snake Optimization model's effectiveness in addressing energy and trust challenges in WSNs, offering a robust solution for secure and efficient data transmission.

The Internet of Things (IoT) has become a popular subject in the technology industry and will soon reach the popularity level of smartphones. With the rapid technological advancements of sensors, Wireless Sensor Networks (WSNs) has become the main technology for IoT. We investigated the security of WSNs in an environmental monitoring system with the goal to improve the overall security. We implemented a Secure Temperature Monitoring System (STMS), which served as our investigational environment. Our results revealed a security flaw found in the bootstrap loader (BSL) password used to protect firmware found in the MSP430 MCU. We demonstrated how the BSL password could be brute forced in a matter of days. Furthermore, to our knowledge we illustrated the first sample of how an attacker can reverse engineer firmware and obtain WSN cryptographic keys. Our sample provides a step-by-step procedure on how to reverse engineer MSP430 firmware. We contributed a solution to improve the BSL password and better protect firmware found in the MSP430 chips. The Secure-BSL software we contributed allows the randomization of the BSL password. Our solution guarantees brute force times in a matter of decades. The impractical brute force time assures the security of firmware and prevents future reverse engineering tactics. In addition, our Secure-BSL software supports two-factor authentication, therefore adding another layer of security. The two-factor authentication feature allows developers to specify a user-defined passphrase to further protect the MSP430 MCU. Our research serves as proof that any security implemented in a WSN environment is broken if an attacker has access to firmware found in sensor devices.

Battery life extension is the main driver for energy- efficient wireless sensor network (WSN) design. Sensor nodes (SNs), as constituents of WSN, are battery-powered devices and have limited amount of energy available. We overcome this limitation by using techniques that reduce the consumption of energy effectively. In this article, we use a technique called duty cycle extension, in order to overcome the problem of time synchronization and save energy. We analyze a case when SN operates under harsh environmental conditions, i.e. when there is large percentage of incorrectly received packets. By applying two low power modes we study how time scales affect duty cycle ratio by varying the time interval between packet transmissions over three orders of magnitude, from 1 second to 3600 seconds. We assume that the number of losses packets varies from 40% up to 70%. For inter-packet intervals from one second to one hour and different SN's low power modes, by implementing a duty cycle extension technique, an increase of battery capacity from 7% up to 323%, per single year working period is achieved.

Wireless sensor networks (WSNs) are considered as a key enabler for the paradigm of Internet of Things (IoT). With increasing number of devices connected to the IoT environments, traditional solutions for WSNs tend to be costly in terms of network maintenance and management. Software-Defined Networking (SDN) appears as a viable alternative network architecture since it enables new services and policies to be deployed flexibly and easily. However, SDN brings excessive control overhead which significantly degrades the network performance. To relieve this problem, in this paper, we propose an Efficient Software-Defined Wireless Sensor Network (ESD-WSN) architecture to make full use of the advantages of SDN while overcoming its constraints. In the proposed architecture, the controller dynamically selects certain nodes as proxies for control traffic processing and aggregating. To this end, a Dynamic Proxy Management (DPM) strategy is presented to select the optimal subset of network nodes as proxies. Experimental results show that our scheme achieves considerable performance improvement compared with the SDN-WISE scheme [1].

Wireless Sensor Network (WSN) is an emerging wireless communication networks to provide potential secure optimized data routing between source and destination. Presently the performance analysis of the Wireless Sensor Network routing technique and security protocols is the major research issues. A major issue in wireless sensor network (WSN) is the energy constraint in a node and its limited computing resources, which may pose an operational hazard or limitations on the network lifetime. Therefore, analyses of innovative secure routing techniques are required to utilize the resources of WSN to improve the life time of network in WSN. To design & develop any innovative wireless sensor network routing and security protocol, the network characteristics & its design issues are important consideration. The proposed spherical Grid Routing Protocol (SGRP) is designed and developed to enhance Quality of service to improve WSN performance using NS2 simulator compare to compared to modified LEACH techniques. The packet transmitted, packet received, average energy, throughput and packet delivery ratio are the main common performance measures that are used for performance analysis of proposed SGRP protocols.

Wireless Sensor Network (WSN) technology has developed substantially over the past decade or so and now numerous solutions exist across a diverse range of innovative applications. The expanding Internet of Things (IoT) sector is becoming an ever more important aspect of modern technology and a key motivator for improving security and privacy in WSNs. Typically, WSN protocols form an integral part of the overall IoT infrastructure by enabling the sensor to access point communication links. These wireless links inherently encompass security challenges, frequently due to external interference and intrusions. As IoT applications incorporate WSNs in their architecture, the incentive to attack and compromise these WSNs escalates. Often, commercial off the shelf devices and standardized open-access protocols combine to achieve specific WSN deployments. Numerous WSN vulnerabilities exist, whilst attack approaches are abundant and change frequently. Thus, to ensure acceptable performance, safety and privacy in many IoT applications, the adopted WSN must be secure. This paper discusses IoT security and privacy, by evaluating a machine learning approach for interference detection focused entirely on analyzing received In-phase (I) and Quadrature-phase (Q) samples. Significantly, once an intrusion is detected, mitigation strategies can be implemented, thus emphasizing the requirement for interference detection. Random Forest is chosen as the machine learning classifier as it consists of a large number of individual decision trees operating as an ensemble. An intrusion detection system (IDS) is developed based on Matlab simulated ZigBee data as an initial insight into whether a real wireless data approach may be viable.

Recently, the demand of Wireless Sensor Network (WSN) increased rapidly due to the vast applications in environmental monitoring, health care, Smart cities, Industrial automation, agriculture and Transports, etc. However, limited battery backup in sensor nodes affects the lifetime of the WSN. In this paper, a novel multi objective optimization problem is formulated to maximize the life time by considering the distance distribution of sensor nodes with their Cluster Heads (CHs) and distribution of CH’s life time. The recent multi objective algorithms like Decomposition based Multi Objective Evolutionary (MOEAD), Multi Objective Grasshopper Algorithm (MOGOA), Multi Objective Particle Swarm Optimization (MOPSO), Multi Objective Grey Wolf optimization (MGWO) algorithm and Improved Multi Objective Differential Evolution (IMODE) algorithms are considered for the designing the multi-objective WSN and their performances are compared based on the scalability, lifetime, energy distribution and cluster distribution of sensor nodes. The simulation results demonstrated that MOEAD based WSN achieves 24.2% more life time than second best algorithms. Further, it achieves 97% (in average) accuracy in cluster quality. Moreover, MOEAD outperformed than other algorithms in Pareto optimal solution in terms of convergence, diversity and solution quality.

Wireless communication networks are highly prone to security threats. The major applications of wireless communication networks are in military, business, healthcare, retail, and transportations. These systems use wired, cellular, or adhoc networks. Wireless sensor networks, actuator networks, and vehicular networks have received a great attention in society and industry. In recent years, the Internet of Things (IoT) has received considerable research attention. The IoT is considered as future of the internet. In future, IoT will play a vital role and will change our living styles, standards, as well as business models. The usage of IoT in different applications is expected to rise rapidly in the coming years. The IoT allows billions of devices, peoples, and services to connect with others and exchange information. Due to the increased usage of IoT devices, the IoT networks are prone to various security attacks. The deployment of efficient security and privacy protocols in IoT networks is extremely needed to ensure confidentiality, authentication, access control, and integrity, among others. In this paper, an extensive comprehensive study on security and privacy issues in IoT networks is provided.

The Internet of things (IoT) and wireless sensor networks (WSNs) have been rapidly and tremendously developing recently as computing technologies have brought about a significant revolution. Their applications and implementations can be found all around us, either individually or collaboratively. WSN plays a leading role in developing the general flexibility of industrial resources in terms of increasing productivity in the IoT. The critical principle of the IoT is to make existing businesses sufficiently intelligent to recognize the need for significant fault mitigation and short-cycle adaptation to improve effectiveness and financial profits. This article presents efficiently applied security protocols at the data link layer for WSN and IoT-based frameworks. It outlines the importance of WSN–IoT applications as well as the architecture of WSN in the IoT. Our primary aim is to highlight the research issues and limitations of WSNs related to the IoT. The fundamental goal of this work is to emphasize a suggested architecture linked to WSN–IoT to enhance energy and power consumption, mobility, information transmission, QoS, and security, as well as to present practical solutions to data link layer difficulties for the future using machine learning. Moreover, we present data link layer protocol issues, attacks, limitations, and research gaps for WSN frameworks based on the recent work conducted on the data link layer concerning WSN applications. Current significant issues and challenges pertain to flow control, quality of service (QoS), security, and performance. In the context of the literature, less work has been undertaken concerning the data link layer in WSN and its relation to improved network performance.