Low-Complexity One-Dimensional Edge Detection in Wireless Sensor Networks

In various wireless sensor network applications, it is of interest to monitor the perimeter of an area of interest. For example, one may need to check if there is a leakage of a dangerous substance. In this paper, we model this as a problem of one-dimensional edge detection, that is, detection of a spatially nonconstant one-dimensional phenomenon, observed by sensors which communicate to an access point (AP) through (possibly noisy) communication links. Two possible quantization strategies are considered at the sensors: (i) binary quantization and (ii) absence of quantization. We first derive the minimum mean square error (MMSE) detection algorithm at the AP. Then, we propose a simplified (suboptimum) detection algorithm, with reduced computational complexity. Noisy communication links are modeled either as (i) binary symmetric channels (BSCs) or (ii) channels with additive white Gaussian noise (AWGN)

Decentralized detection in IEEE 802.15.4 wireless sensor networks

We present a mathematical model to study decentralized detection in clustered wireless sensor networks (WSNs). Sensors and fusion centers (FCs) are distributed with the aim of detecting an event of interest. Sensors are organized in clusters, with FCs acting as cluster heads, and are supposed to observe the same common binary phenomenon. A query-based application is accounted for; FCs periodically send queries and wait for replies coming from sensors. After reception of data, FCs perform data fusion with a majority-like fusion rule and send their decisions to an access point (AP), where a final data fusion is carried out and an estimate of the phenomenon is obtained. We assume that sensors are IEEE 802.15.4-compliant devices and use the medium access control (MAC) protocol defined by the standard, based on carrier-sense multiple access with collision avoidance. Decentralized detection and MAC issues are jointly investigated through analytical modelling. The proposed framework allows the derivation of the probability of decision error at the AP, when accounting for packets' losses due to possible collisions. Our results show that MAC losses strongly affect system performance. The impact of different clustering configurations and of noisy communications is also investigated

On driver behavior recognition for increased safety:A roadmap

Advanced Driver-Assistance Systems (ADASs) are used for increasing safety in the automotive domain, yet current ADASs notably operate without taking into account drivers’ states, e.g., whether she/he is emotionally apt to drive. In this paper, we first review the state-of-the-art of emotional and cognitive analysis for ADAS: we consider psychological models, the sensors needed for capturing physiological signals, and the typical algorithms used for human emotion classification. Our investigation highlights a lack of advanced Driver Monitoring Systems (DMSs) for ADASs, which could increase driving quality and security for both drivers and passengers. We then provide our view on a novel perception architecture for driver monitoring, built around the concept of Driver Complex State (DCS). DCS relies on multiple non-obtrusive sensors and Artificial Intelligence (AI) for uncovering the driver state and uses it to implement innovative Human–Machine Interface (HMI) functionalities. This concept will be implemented and validated in the recently EU-funded NextPerception project, which is briefly introduced

Feedback Power Control Strategies in Wireless Sensor Networks with Joint Channel Decoding

In this paper, we derive feedback power control strategies for block-faded multiple access schemes with correlated sources and joint channel decoding (JCD). In particular, upon the derivation of the feasible signal-to-noise ratio (SNR) region for the considered multiple access schemes, i.e., the multidimensional SNR region where error-free communications are, in principle, possible, two feedback power control strategies are proposed: (i) a classical feedback power control strategy, which aims at equalizing all link SNRs at the access point (AP), and (ii) an innovative optimized feedback power control strategy, which tries to make the network operational point fall in the feasible SNR region at the lowest overall transmit energy consumption. These strategies will be referred to as “balanced SNR” and “unbalanced SNR,” respectively. While they require, in principle, an unlimited power control range at the sources, we also propose practical versions with a limited power control range. We preliminary consider a scenario with orthogonal links and ideal feedback. Then, we analyze the robustness of the proposed power control strategies to possible non-idealities, in terms of residual multiple access interference and noisy feedback channels. Finally, we successfully apply the proposed feedback power control strategies to a limiting case of the class of considered multiple access schemes, namely a central estimating officer (CEO) scenario, where the sensors observe noisy versions of a common binary information sequence and the AP's goal is to estimate this sequence by properly fusing the soft-output information output by the JCD algorithm

Sensor networks with decentralized binary detection: clustering and lifetime

Abstract — In this paper, we analyze the lifetime of clustered sensor networks with decentralized binary detection under a physical layer quality of service (QoS) constraint, given by the maximum tolerable probability of decision error at the access point (AP). In order to properly model the network behavior, we consider four different distributions (exponential, uniform, Rayleigh, and lognormal) for the single sensors ’ lifetime. We show the benefits, in terms of longer network lifetime, of adaptive reclustering. On the other hand, absence of reclustering leads to a shorter network lifetime, and we show the impact of various clustering configurations under different QoS conditions. Our results show that the organization of sensors in a few big clusters is the winning strategy to maximize the network lifetime. I

A hybrid radio/accelerometric approach to arm posture recognition

In this paper, we investigate the feasibility of a hybrid radio/accelerometric approach to perform arm posture recognition. A radio fingerprinting-based approach, through measurements of the Received radio Signal Strengths (RSSs) from anchor nodes, is first used to localize the positions (among a set determined during a training phase) of target nodes properly placed on a user arm. Accelerometric signals generated by the target nodes are then used to estimate the pitch of every device in order to refine the radio fingerprinting results and perform posture recognition, i.e., “continuous” estimation of the positions of the target nodes. We experimentally investigate, through a SunSPOT wireless sensor network testbed, different fingerprinting-based localization algorithms, namely deterministic and probabilistic. In each case, the system parameters are optimized by minimizing a properly defined Position Error (PE). Finally, a comparison between the performance of the proposed system and that of a low-cost optical arm posture recognition system (namely, Kinect) is presented

Impact of the knowledge of nodes’ positions on spectrum sensing strategies in cognitive networks

In this paper, we focus on cognitive wireless networking, where a primary wireless network (PWN) is co-located with a cognitive (or secondary) wireless network (CWN). The shared frequency spectrum is divided into disjoint “subchannels” and each subchannel is “freely” assigned (in a unique way) to a node of the PWN, denoted as primary user equipment (PUE). We assume that the nodes of the CWN, denoted as cognitive user equipments (CUEs), cooperate to sense the frequency spectrum and estimate the idle subchannels which can be used by the CWN (i.e., assigned to CUEs) without interfering the PWN. The sensing correlation among the CUEs is exploited to improve the reliability of the decision, taken by a secondary fusion center (FC), on the occupation status (by a node of the PWN) of each subchannel. In this context, we compute the mutual information between the occupation status and the observations at the FC, with and without knowledge of the positions of the nodes in the network, showing a potential significant benefit brought by this side information. Then, we derive the fusion rules at the FC: our numerical results, in terms of the network-wise probabilities of missed detection (MD) and false alarm (FA) at the secondary FC, indicate a significant performance improvement when knowledge of the CUEs’ positions is available at the secondary FC, confirming the mutual information-based theoretical prediction