To gather and transmit data, low cost wireless devices are often deployed in open, unattended and possibly hostile environment, making them particularly vulnerable to physical attacks. Resilience is needed to mitigate such inherent vulnerabilities and risks related to security and reliability. In this paper, Routing Protocol for Low-Power and Lossy Networks (RPL) is studied in presence of packet dropping malicious compromised nodes. Random behavior and data replication have been introduced to RPL to enhance its resilience against such insider attacks. The classical RPL and its resilient variants have been analyzed through Cooja simulations and hardware emulation. Resilient techniques introduced to RPL have enhanced significantly the resilience against attacks providing route diversification to exploit the redundant topology created by wireless communications. In particular, the proposed resilient RPL exhibits better performance in terms of delivery ratio (up to 40%), fairness and connectivity while staying energy efficient.1550-445X/15 $31.00

This paper proposes a proof-of-concept, low-cost, and easily deployable Bluetooth low energy- (BLE-) based localization system which actively scans and localizes BLE beacons attached to mobile subjects in a room. Using the received signal strength (RSS) of a BLE signal and the uniqueness of BLE hardware addresses, mobile subjects can be identified and localized within the hospital room. The RSS measurement of the BLE signal from a wearable BLE beacon varies with distance to the wall-anchored BLE scanner. In order to understand and demonstrate the practicality of the relationship between RSS of a BLE beacon and the distance of a beacon from a scanner, the first part of the paper presents the analysis of the experiments conducted in a low-noise and nonreflective environment. Based on the analysis conducted in an ideal environment, the second half of the paper proposes a data-driven localization process for pinpointing the movements of the subject within the experimental room. In order to ensure higher accuracy like fingerprinting techniques and handle the increased number of BLE-anchored scanners like geometric techniques, the proposed algorithm was designed to combine the best aspects of these two techniques for better localization. The paper evaluates the effects of the number of BLE wall-mounted scanners and the number of packets on the performance of the proposed algorithm. The proposed algorithm locates the patient within the room with error less than 1.8 m. It also performs better than other classical approaches used in localization.

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