Routing Protocol for Low-Power and Lossy Networks (RPL) is an energy-efficient routing solution for IPv6 over Low-Power Wireless Personal Area Networks (6LoWPAN), recommended for resource-constrained devices. While RPL offers significant benefits, its security vulnerabilities pose challenges, particularly due to unauthenticated control messages used to establish and maintain routing information. These messages are susceptible to manipulation, enabling malicious nodes to inject false routing data. A notable security concern is the Routing Table Falsification (RTF) attack, where attackers forge Destination Advertisement Object (DAO) messages to promote fake routes via a parent node's routing table. Experimental results indicate that RTF attacks significantly reduce packet delivery ratio, increase end-to-end delay, and leverage power consumption. Currently, no effective countermeasures exist in the literature, reinforcing the need for a security solution to prevent network disruption and protect user applications. This paper introduces aLightweightSecurity Solution againstRoutingTableFalsification Attack (LiSec-RTF), leveraging Physical Unclonable Functions (PUFs) to generate unique authentication codes, termed “Licenses.” LiSec-RTF mitigates RTF attack impact while considering the resource limitations of 6LoWPAN devices in both static and mobile scenarios. Our testbed experiments indicate that LiSec-RTF significantly improves network performance compared to standard RPL under RTF attacks, thereby ensuring reliable and efficient operation.

This study presents a smart‐phone‐based hybrid indoor localisation system (HILS), which integrates two different localisation approaches, i.e., pedestrian dead reckoning (PDR) and Wi‐Fi‐based localisation to compensate the limits of each other. In PDR, the localisation accuracy degrades continuously when the moving distance increases from the starting reference point. However, Wi‐Fi‐based trilateration method requires the Wi‐Fi signals from at least three access points (APs). In this study, the authors propose a more efficient novel Heron‐bilateration‐based position determination (HBPD) technique, which requires the Wi‐Fi signals from only two APs to locate the mobile device. The proposed HILS utilises the HBPD technique to reset the bias drift errors involved with inertial sensors, and compensate the unavailability of strong Wi‐Fi signals from APs using the PDR method. In this work, an experimental testbed is developed to collect the Wi‐Fi signals and evaluate the proposed method. The proposed HILS is also compared with five other benchmark methods for two different trajectories and the experimental results show that the mean localisation error is decreased up to 1.68 and 1.96 m, which indicate the improvement by 16.83 and 20.97% for both the trajectories, respectively, in comparison with the best performing method among the benchmark methods.

In wireless sensor networks (WSNs), sink mobility requires frequent advertisement of current location of sink across the network which may introduce an overhead in terms of traffic load, collision, and energy consumption. Hence, an energy efficient routing protocol is required to minimize these overheads for reliable data delivery in the network. This paper proposes a load balanced ring routing (LBRR) protocol for the large scale heterogeneous sensor networks with sink mobility support. The design objective of proposed protocol is to balance the traffic load towards the mobile sink in a large densely deployed network. The simulation results show that the proposed protocol is an energy efficient routing protocol in case of heavy traffic load. The packet drop ratio is significantly reduced as compared to benchmark ring routing protocol by 84.36%, 82.75%, 83.5% and 82.9% when sink mobility is 1, 5, 10 and 15 km#x002F;h respectively.

Today's precision agriculture needs advance methods and technology to reduce cost and improve productivity. This paper explores the potential use of wireless sensor network in precision agriculture. An energy efficient network layer routing protocol is required to maximize the lifetime of sensor network. The proposed Threshold Sensitive Region-Based Hybrid Routing (TS-RBHR) protocol uses region-based static clustering approach to provide efficient coverage of agricultural area. The fuzzy based hybrid routing approach is used for transmitting sensed data to base station which minimizes the energy consumption of nodes. Sensor nodes continuously sense temperature and soil moisture content of agricultural field and if sensed value exceeds the desired threshold, a data packet is sent to the base station which reduces the continuous transmission rate. The experimental results show that proposed protocol has a significant increase in network lifetime due to reduction in frequent data transmission.

Location Estimation has become important for many applications of indoor wireless networks. Received Signal Strength (RSS) fingerprinting methods have been widely used for location estimation. The accuracy and response time of estimation are critical issue in location estimation system. Most of the location estimation system suffers with the problem of scalability and unavailability of all the access points at all the location for large site. In this paper, we have proposed a distributed location estimation method, which divide the location estimation system into subsystems. Our method partition the input signal space and output location space into clusters on the basis of visibility of access points at various locations of the site area. Each cluster of input signal space together with output location subspace is used to learn the association between RSS fingerprint and their respective location in a subsystem. We have compared our results with benchmark RADAR method. Experimental results show that our method provide better results in terms of accuracy and response time in comparison to centralized systems, in which a single system is used for large site.