Wireless sensor networks (WSNs) are an emerging technology used in many applications in both the civilian and military domains. Typically, these networks are deployed in remote and hostile environments. They are vulnerable to various kinds of security attacks, of which sybil attacks are some of the most harmful. Thus, it is necessary to solve the problems related to sensor node constraints and the need for high WSN security. This paper proposes an energy trust system (ETS) for WSNs to effectively detect sybil attacks. It employs multi-level detection based on identity and position verification. Then, a trust algorithm is applied based on the energy of each sensor node. Data aggregation is also utilized to reduce communication overhead and save energy. We analyze the performance of the proposed system in terms of security and resource consumption using theoretical and simulation-based approaches. The simulation results show that the proposed ETS is effective and robust in detecting sybil attacks in terms of the true and false positive rates. By virtue of the application of multi-level detection, the proposed system achieves more than 70% detection at the first level, which significantly increases to 100% detection at the second level. Furthermore, this system reduces communication overhead, memory overhead, and energy consumption by eliminating the exchange of feedback and recommendation messages among sensor nodes.

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As a possible implementation of a low-power wide-area network (LPWAN), Long Range (LoRa) technology is considered to be the future wireless communication standard for the Internet of Things (IoT) as it offers competitive features, such as a long communication range, low cost, and reduced power consumption, which make it an optimum alternative to the current wireless sensor networks and conventional cellular technologies. However, the limited bandwidth available for physical layer modulation in LoRa makes it unsuitable for high bit rate data transfer from devices like image sensors. In this paper, we propose a new method for mangrove forest monitoring in Malaysia, wherein we transfer image sensor data over the LoRa physical layer (PHY) in a node-to-node network model. In implementing this method, we produce a novel scheme for overcoming the bandwidth limitation of LoRa. With this scheme the images, which requires high data rate to transfer, collected by the sensor are encrypted as hexadecimal data and then split into packets for transfer via the LoRa physical layer (PHY). To assess the quality of images transferred using this scheme, we measured the packet loss rate, peak signal-to-noise ratio (PSNR), and structural similarity (SSIM) index of each image. These measurements verify the proposed scheme for image transmission, and support the industrial and academic trend which promotes LoRa as the future solution for IoT infrastructure.

The routing protocol for Wireless Sensor Networks (WSNs) is defined as the manner of data dissemination from the network field (source) to the base station (destination). Based on the network topology, there are two types of routing protocols in WSNs, they are namely flat routing protocols and hierarchical routing protocols. Hierarchical routing protocols (HRPs) are more energy efficient and scalable compared to flat routing protocols. This paper discusses how topology management and network application influence the performance of cluster-based and chain-based hierarchical networks. It reviews the basic features of sensor connectivity issues such as power control in topology setup , sleep/idle pairing and data transmission control that are used in five common HRPs, and it also examines their impact on the protocol performance. A good picture of their respective performances give an indication how network applications, i.e whether reactive or proactive, and topology management i.e. whether centralized or distributed would determine the network performance. Finally, from the ensuring discussion, it is shown that the chain-based HRPs guarantee a longer network lifetime compared to cluster-based HRPs by three to five times.