Time synchronisation is a fundamental requirement for the IEEE802.15.4e‐based industrial Internet of Things, allowing it to reach low‐power and high‐reliability wireless network through time synchronisation. If malicious adversaries launch time synchronisation attacks to IEEE802.15.4e‐based networks, the entire network communication will be paralysed. In this study, the authors introduce two types of time synchronisation attacks: (i) absolute slot number (ASN) and (ii) timeslot template attack. In ASN attack, the new nodes may receive an incorrect ASN value, which prevents synchronisation with the typical network, while in the timeslot template attack, the malicious node can make the legitimate nodes calculate the error clock offset. The authors propose an enhanced secure time synchronisation protocol to defend against these attacks, which include Sec_ASN and TOF algorithm. The Sec_ASN and threshold filter (TOF) algorithms are used to defend against ASN attack and timeslot template attacks, respectively. Finally, the authors run a thorough set of simulations to assess the effectiveness of the proposed attacks as well as the proposed countermeasure. The results show that the attacks can significantly destroy the time synchronisation protocol, but can be successfully defended using the proposed mechanisms.

The detection of abnormal moving objects over high-volume trajectory streams is critical for real-time applications ranging from military surveillance to transportation management. Yet this outlier detection problem, especially along both the spatial and temporal dimensions, remains largely unexplored. In this work, we propose a rich taxonomy of novel classes of neighbor-based trajectory outlier definitions that model the anomalous behavior of moving objects for a large range of real-time applications. Our theoretical analysis and empirical study on two real-world datasets—the Beijing Taxi trajectory data and the Ground Moving Target Indicator data stream—and one generated Moving Objects dataset demonstrate the effectiveness of our taxonomy in effectively capturing different types of abnormal moving objects. Furthermore, we propose a general strategy for efficiently detecting these new outlier classes called the minimal examination (MEX) framework. The MEX framework features three core optimization principles, which leverage spatiotemporal as well as the predictability properties of the neighbor evidence to minimize the detection costs. Based on this foundation, we design algorithms that detect the outliers based on these classes of new outlier semantics that successfully leverage our optimization principles. Our comprehensive experimental study demonstrates that our proposed MEX strategy drives the detection costs 100-fold down into the practical realm for applications that analyze high-volume trajectory streams in near real time.

Time synchronization is very important in the IEEE802.15.4e network which aim to industrial automation applications. It enabled high end-to-end reliability and low power wireless networking. If an adversary launches time synchronization attacks to the IEEE802.15.4e networks, the whole network communications will be paralyzed. In this paper, we present two types of attacks: 1) ASN and 2) time synchronization tree attack. In ASN attack the legitimate nodes may get an incorrect ASN value and thus can't synchronize to the normal network, while in time synchronization tree attack, the attacker can damage the structure of time synchronization tree by faking DIO packets. We propose some countermeasures which include intrusion detection algorithms, Encryption and Authentication methods to defend against these attacks. Finally, we perform time synchronization tree attack experiments. The experiment results show that the proposed mechanisms can defend against the attack.

The 6TiSCH working group is standardizing the low-power wireless protocol stack for the Industrial IoT. The default scheduling function (SF0) standardized by 6TiSCH uses simple random slot selection. This paper proposes the Low Latency Scheduling Function (LLSF), a new scheduling function which daisy-chains timeslots rather than picking them randomly. We implement LLSF in OpenWSN and evaluate its performance experimentally. LLSF yields 82.8% lower end-to-end latency ona 5-hop path than SF0, at no extra costs.

Typically, anchor-free localization is considered as a nonlinear programming problem in the existing literatures. However, the nonlinear programming algorithms can only achieve constrained optimization and the localization accuracy of such algorithms depends on the precision of initial coordinators, which are the inputs of the algorithm and usually obtained based on GPS. Due to this defect, the algorithm is invalid in GPS-denied area, such as indoor area, dense urban area and forest. In our research, we combined nonlinear programming algorithm with penalty function to solve this problem. Our simulation results show that the localization accuracy of proposed algorithm is not affected by the precision of the initial coordinators, even when the initial coordinators is set randomly. Performance comparisons are also presented to show the improvement of this algorithm.

The detection of abnormal moving objects over high-volume trajectory streams is critical for real time applications ranging from military surveillance to transportation management. Yet this problem remains largely unexplored. In this work, we first propose classes of novel trajectory outlier definitions that model the anomalous behavior of moving objects for a large range of real time applications. Our theoretical analysis and empirical study on the Beijing Taxi and GMTI (Ground Moving Target Indicator) datasets demonstrate its effectiveness in capturing abnormal moving objects. Furthermore we propose a general strategy for efficiently detecting the new outlier classes. It features three fundamental optimization principles designed to minimize the detection costs. Our comprehensive experimental studies demonstrate that our proposed strategy drives the detection costs 100-fold down into practical realm for applications producing high volume trajectory streams to utilize.

Industrial low-power wireless mesh networks are shifting towards time-synchronized medium access control (MAC) protocols which are able to yield over 99.9% end-to-end reliability and radio duty cycles well below 1%. In these networks, motes use time slots to communicate, and neighbor motes maintain their clocks' alignment, typically within 1 ms. Temperature, supply voltage, and fabrication differences cause the motes' clocks to drift with respect to one another. Neighbor motes need to re-synchronize periodically through pairwise communication. This period is typically determined a priori, based on the worst case drift. In this paper, we propose a novel technique which measures and models the relative clock drift between neighbor motes, thereby reducing the effective drift rate. Instead of resynchronizing at a preset rate, neighbor motes resynchronize only when needed. This reduces the minimum achievable duty cycle of an idle network by a factor of 10, which in turn lowers the mote power consumption and extends the network lifetime. This Adaptive Synchronization is implemented as part of IEEE802.15.4e in the OpenWSN protocol stack and is validated through extensive experimentation.

Performance evaluation is an essential step in the development and deployment of an indoor TOA location system, in order to see the influence of multipath condition on ranging and localization accuracy. For field testing approach, multipath condition is invisible, uncontrollable and unrepeatable. Also it is usually not convenient due to system deployment. This paper presents a multipath visible, controllable and repeatable realtime testbed for indoor TOA location performance evaluation, based on RF channel emulator and Ray-tracing software. The testbed can provide accurate performance evaluation in specific indoor environments or specific multipath conditions without deployment. We evaluate the performance of an IEEE 802.15.4a Standard location system in typical office building with this testbed. The result was compared with the performance obtained in field testing to validate the feasibility of the testbed approach.

This paper presents an practical RSSI based TOA ranging error model (RITEM) for localization algorithm, which can be used to estimate ranging error interval in real time. In RITEM, ranging error is classified into four classes by the RSSI value in TOA ranging process and ranging error of each class always within a certain interval. RITEM is verified by field tests in two typical indoor environments. Then, RITEM is applied into Ranging Error Classification (REC) based TOA localization algorithm to introduce its application methodology. Experiment result indicates that REC algorithm has significantly improved performance in typical indoor environment, comparing with LS, CN-TOAG and Nano localization algorithms.

Wireless sensor networks provide an opportunity to enhance the current equipment diagnosis systems in the process industry, which have been based so far on wired networks. In this paper, we use our experience in the Anshan Iron and Steel Factory, China, as an example to present the issues from the real field of process industry, and our solutions. The challenges are three fold: First, very high reliability is required; second, energy consumption is constrained; and third, the environment is very challenging and constrained. To address these issues, it is necessary to put systematic efforts on network topology and node placement, network protocols, embedded software, and hardware. In this paper, we propose two technologies i.e. design for reliability and energy efficiency (DRE), and design for reconfiguration (DRC). Using these techniques we developed Anshan, a wireless sensor network for monitoring the temperature of rollers in a continuously annealing line and detecting equipment failures. Project Anshan includes 406 sensor nodes and has been running for four months continuously.