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In path-constrained multi-hop sensor networks (M-WSNs), maximizing data collection with minimal energy consumption is critical, especially for delay-sensitive applications. Because a mobile element (ME) moving at a constant speed along a constrained-path must receive data from sensor nodes (SNs) within a given time bound. This issue can be addressed by efficiently scheduling the SNs' data transmission. The shortest path data transmission (SPT) or its variations are a simple and energy-efficient technique for scheduling data transmission from SNs. Although this method considerably minimizes total energy spent, it does not enhance data collection efficiency due to uneven data forwarding load. To solve this issue, this paper proposes a novel efficient heuristic method. The proposed method first computes a set of discrete sub-paths on a given path based on the start and end distance points of nearby SNs, the speed of ME, and the given time delay. Then, for each sub-path and SN-communication model, a network flow graph is used to schedule and optimize SN data transmission to ME. Finally, a sub-path is chosen among those which return maximum data with minimum energy consumption. The network flow graphs are created by considering two distinct SN-communication models: (1) energy-unrestricted SN-model and (2) energy-restricted SN-model. Finally, the simulation reports demonstrate the proposed method's efficacy over baseline schemes in terms of data collected, energy usage efficiency, and success-delivery ratio. [ABSTRACT FROM AUTHOR]

In wireless sensor networks, a Mobile Collector (MC) is used to gather data by periodically traversing the network to avoid hotspot or energy-hole issues. Although the MC's data collection process and network performance can be enhanced by determining suitable set of Stop Points (SPs), it is challenging to find the best set of SPs and schedule an effective MC trajectory. Much attention has been received to MC's path planning through SPs in a static environment where the path is determined during the initial phase, but they do not emphasize the nodes' coverage rate and cannot be adapted to network topology changes. In this context, we propose an Efficient Trajectory Planning method for Coverage Enhanced Data collection in WSN (ETP-CED). We introduce an enhanced method based on integrated Particle Swarm Optimization and Ant Colony Optimization for selecting the best set of SPs and planning efficient MC trajectory. ETP-CED is adaptive to node failures, allowing the nodes to reposition themselves to patch up coverage holes in the network. MC readjusts its planned path when there are less nodes in the network due to node failures, thereby shortening the trajectory length and speeding up data delivery. The results show that ETP-CED outperforms existing methods in the aspects of nodes' coverage and data collection efficiency. [ABSTRACT FROM AUTHOR]

Tracking mobile objects in road networks is one of the important applications of wireless sensor networks (WSNs). It includes road safety, smart transportation, vehicle safety, vehicle tracking, traffic monitoring, etc. In this work, we assume that the sensors are installed on vehicles which are moving on the road. These mobile sensors need to be tracked/visited periodically within a pre-defined time interval. In our scenario, the mobility or trajectory of the mobile sensors are unpredictable. In many applications nowadays, the mobile sensors have unpredictable mobility. In this paper, our aim is to collect data by visiting the mobile sensors periodically using fewer number of mobile sinks. The mobile sinks subsequently deliver the collected data to a stationary base station. Time-bound periodic data collection from the mobile sensors along with their unpredictable motion is even more challenging than the data collection from static sensors. Here, we propose a deterministic data gathering algorithm using the solution of Chinese Postman Problem. We measure the performance of the proposed solution. Due to non-availability of any existing solution, we have compared our algorithm with a heuristic algorithm and a variation of an existing solution. The experiment results show that our proposed data gathering algorithm performs well. [ABSTRACT FROM AUTHOR]

The Internet of Things (IoT), including wireless sensors, is one of the highly anticipated contributors to big data; therefore, avoiding misleading or forged data gathering in cases of sensitive and critical data through secure communication is vital. Wireless sensor networks are relatively simple, scalable networks with many applications in research. They can provide many benefits, including ad hoc distribution, lower costs, and higher flexibility. In a scenario where time is of the essence and dedicated base stations cannot be established, mobile sinks must be used to gather data. IoT systems are based on a collective organization in which devices collaborate to provide better and more accurate decisions. It is important to ensure that the information being shared is legitimate to avoid any significant degradation in system performance because of false or inaccurate information. Building trust—the "assurance" between two devices that the information being shared can be used with confidence that it is accurate—will create a trustworthy, secure system in which all devices are identified and no information is accepted from any unauthorized device. The key contribution of this work is a new, dynamic, trust-based clustering mechanism by which nodes can securely connect to one another and begin transmitting data to a sink while it is available. To demonstrate the utility of this mechanism, we examine two possible attacks on a trust-based network and present a heuristic solution for minimizing the negative effects of such attacks in an energy-efficient way. Our results show improved network performance through reduction of the number of cycles required to isolate or mitigate the effect of malicious nodes in the network, thus reducing the energy consumption in the network with a concomitant increase in its lifespan. Our cluster methodology also has the effect of spreading energy consumption among nodes, thereby reducing early fall-off of nodes and network holes. [ABSTRACT FROM AUTHOR]

Enhancing the network lifetime of wireless sensor networks is an essential task. It involves sensor deployment, cluster formation, routing, and effective utilization of battery units. Clustering and routing are important techniques for adequate enhancement of the network lifetime. Since the existing clustering and routing approaches have high message overhead due to forwarding collected data to sinks or the base station, it creates premature death of sensors and hot-spot issues. The objective of this study is to design a dynamic clustering and optimal routing mechanism for data collection in order to enhance the network lifetime. A new dynamic clustering approach is proposed to prevent premature sensor death and avoid the hot spot problem. In addition, an Ant Colony Optimization (ACO) technique is adopted for effective path selection of mobile sinks. The proposed algorithm is compared with existing routing methodologies, such as LEACH, GA, and PSO. The simulation results show that the proposed cluster head selection algorithm with ACO-based MDC enhances the sensor network lifetime significantly. [ABSTRACT FROM AUTHOR]

This paper proposes a secure encrypted-data aggregation scheme for wireless sensor networks. Our design for data aggregation eliminates redundant sensor readings without using encryption and maintains data secrecy and privacy during transmission. Conventional aggregation functions operate when readings are received in plaintext. If readings are encrypted, aggregation requires decryption creating extra overhead and key management issues. In contrast to conventional schemes, our proposed scheme provides security and privacy, and duplicate instances of original readings will be aggregated into a single packet. Our scheme is resilient to known-plaintext attacks, chosen-plaintext attacks, ciphertext-only attacks and man-in-the-middle attacks. Our experiments show that our proposed aggregation method significantly reduces communication overhead and can be practically implemented in on-the-shelf sensor platforms. [ABSTRACT FROM AUTHOR]