Singapore has the fastest ageing population in the Asia Pacific region, with an estimated 82,000 seniors living with dementia. These figures are projected to increase to more than 130,000 by 2030. The challenge is to identify more community dwelling seniors with Mild Cognitive Impairment (MCI), a prodromal state, as it provides an opportunity for evidence-based early intervention to delay the onset of dementia. In this paper, we explore the use of Internet of Things (IoT) systems in detecting MCI symptoms in seniors who are living alone, and accurately grouping them into MCI positive and negative subjects. We present feature extraction methods and findings from real data captured via selected sensors installed in the homes of 49 seniors for up to two months. Performance evaluation shows that the sleep state variability, as measured through bed sensors, yields a recall of over 70% in predicting MCI in these community dwelling seniors.

At the Bloomberg Live `Sooner Than You Think' forum [1] held in Singapore in 2018, nearly 75% of delegates picked inclusiveness to be the key measure of success for a smart city. An inclusive smart city is a citizen-centered approach that extends the experiences provided by smart city solutions to all citizens, including seniors and persons with disabilities (PwDs).Despite existing regulations on barrier-free accessibility for buildings and public infrastructure, pedestrian infrastructure is generally still inaccessible to PwDs in many parts of the world. In this paper, we present SmartBFA (Smart Mobility and Accessibility for Barrier Free Access) - a publicly-funded initiative in Singapore that aims to design a scalable and sustainable system that can collect, classify and determine accessible point-to-point routes to address interconnection gaps in first and last mile BFA paths for persons requiring barrier-free access (such as wheelchair users and seniors with mobility aids).In SmartBFA, point-to-point accessibility information is passively crowdsourced from IoT devices that are retrofitted on the wheelchairs of participants, as they go about their daily commute. We share preliminary findings from data acquired from 68 wheelchair participants between May 2018 to Mar 2019, spanning across 23,000 hrs and 40,000 km of traveled paths. We compare travel patterns of participants with varying wheelchair types, as well as demonstrate the feasibility and scalability of such a crowdsourced approach for acquiring accessibility data.

The dynamic and unpredictable nature of energy harvesting sources that are used in wireless sensor networks necessitates the need for adaptive duty cycling techniques. Such adaptive control allows sensor nodes to achieve energy-neutrality, whereby both energy supply and demand are balanced. This paper proposes a framework enabling an adaptive duty cycling scheme for sensor networks that takes into account the operating duty cycle of the node, and application-level QoS requirements. We model the system as a Continuous Time Markov Chain (CTMC), and derive analytical expressions for key QoS metrics - such as latency, loss probability and power consumption. We then formulate and solve the optimal operating duty cycle as a non-linear optimization problem, using latency and loss probability as the constraints. Simulation results show that a Markovian duty cycling scheme can outperform periodic duty cycling schemes.

The dynamic and unpredictable nature of energy harvesting sources available for wireless sensor networks, and the time variation in network statistics like packet transmission rates and link qualities, necessitate the use of adaptive duty cycling techniques. Such adaptive control allows sensor nodes to achieve long-run energy neutrality, where energy supply and demand are balanced in a dynamic environment such that the nodes function continuously. In this paper, we develop a new framework enabling an adaptive duty cycling scheme for sensor networks that takes into account the node battery level, ambient energy that can be harvested, and application-level QoS requirements. We model the system as a Markov decision process (MDP) that modifies its state transition policy using reinforcement learning. The MDP uses continuous time Markov chains (CTMCs) to model the network state of a node to obtain key QoS metrics like latency, loss probability, and power consumption, as well as to model the node battery level taking into account physically feasible rates of change. We show that with an appropriate choice of the reward function for the MDP, as well as a suitable learning rate, exploitation probability, and discount factor, the need to maintain minimum QoS levels for optimal network performance can be balanced with the need to promote the maintenance of a finite battery level to ensure node operability. Extensive simulation results show the benefit of our algorithm for different reward functions and parameters.

Abstract The inherent temporal connectivity and existence of impairments in wireless channels pose challenges to network performance. Cooperative communication has been proposed as an effective technique to mitigate the imperfections of the wireless medium by exploiting channel diversity and availability of neighboring nodes that can act as relays. Although numerous cooperative communication techniques have been proposed in the literature, most of them do not consider Quality of Service (QoS) issues in a wireless sensor network. In this work, we study how cooperative communication can be applied to achieve differentiated QoS in a sensor network that uses Ultra‐Wideband (UWB) as its underlying PHY layer technology. SAUCeR is a slotted‐aloha based ultra‐wideband medium access control protocol with cooperative retransmissions that provides differentiated QoS in networks with varying traffic classes. Despite the high transmission rates provided by UWB, its impulse‐based nature renders many conventional carrier sensing MAC protocols incompatible. Consequently, SAUCeR utilizes slotted‐aloha to reduce packet collisions without the need for carrier sensing. Differentiated QoS is provided by allocating different resources (time slots) to varying traffic classes to segregate the contention between them. A QoS‐aware cooperative retransmission technique and two distributed relay selection schemes are also introduced to improve overall traffic throughput and reduce end‐to‐end delay, while preventing the starvation of any traffic class. Copyright © 2010 John Wiley & Sons, Ltd.

Upon the occurrence of a phenomenon of interest in a wireless sensor network, multiple sensors may be activated, leading to data implosion and redundancy. Data aggregation and/or fusion techniques exploit spatio-temporal correlation among sensory data to reduce traffic load and mitigate congestion. However, this is often at the expense of loss in Information Quality (IQ) of data that is collected at the fusion center. In this work, we address the problem of finding the least-cost routing tree that satisfies a given IQ constraint. We note that the optimal least-cost routing solution is a variation of the classical NP-hard Steiner tree problem in graphs, which incurs high overheads as it requires knowledge of the entire network topology and individual IQ contributions of each activated sensor node. We tackle these issues by proposing: (i) a topology-aware histogram-based aggregation structure that encapsulates the cost of including the IQ contribution of each activated node in a compact and efficient way; and (ii) a greedy heuristic to approximate and prune a least-cost aggregation routing path. We show that the performance of our IQ-aware routing protocol is: (i) bounded by a distance-based aggregation tree that collects data from all the activated nodes; and (ii) comparable to another IQ-aware routing protocol that uses an exhaustive brute-force search to approximate and prune the least-cost aggregation tree.

Energy constraints in wireless sensor nodes necessitate the design and development of energy-efficient MAC protocols to arbitrate access to the shared communication medium. While there exists a plethora of sensor MAC protocols, these protocols do not individually vary the duty-cycle of each sensor according to local connectivity status, to maximize energy savings. In this paper, we propose A2-MAC - an Adaptive, Anycast MAC protocol for low-powered wireless sensor networks. It utilizes: (i) a random wakeup schedule, such that each node can independently and randomly wakeup in each cycle without coordination and time synchronization; (ii) adaptive duty-cycles based on network topology; and (iii) adaptive anycast forwarders selection, which allows each node to transmit to any member in its forwarding set. There are two key adaptive mechanisms in A2-MAC: (i) each node varies its duty-cycle and set of forwarding nodes such that energy consumption can be locally minimized for a given local delay performance objective; and (ii) nodes cooperatively reduce the duty-cycles required of their forwarding nodes, depending on local network connectivity. By allowing nodes to operate with different duty-cycles and forwarding sets, A2-MAC achieves better energy-latency tradeoffs and extends node lifetime substantially, while providing good end-to-end latency.

Ultra-wideband (UWB) is a physical (PHY) layer technology that promises high transmission rates, as well as high resistance to noise and multipath effects. However, the impulse-based nature of UWB, coupled with its low transmission power, makes it difficult to enable efficient detection of the signals. Consequently, conventional carrier-sensing based MAC protocols cannot be used with a UWB PHY. In this paper, we propose SASW-CR - a Slotted Aloha MAC protocol for UWB networks with Sliding contention Window and Coperative Retransmissions, which provides QoS support without the use of carrier sensing. SASW-CR utilizes the slotted-Aloha technique to avoid carrier sensing and reduce packet collisions. In addition, it makes use of differentiated contention windows to provide varying classes of QoS for different traffic classes. A cooperative retransmission technique is also introduced to improve the overall traffic throughput and reduce end-to-end delay. The efficacy of our protocol is demonstrated through simulations.

The inherent near-far effect in wireless networks causes nodes that are further away from the receiver to suffer from throughput degradation, as packets from nodes that are nearer are typically received with greater signal strengths. This unfair situation is traditionally overcome by power control. However, when power control is not feasible, for example in tiny sensor nodes with power-limited batteries, alternative solutions have to be utilized to achieve fairness in the network. In this paper, we propose U-LiBRA - an UWB Location Based Resource Allocation scheme to alleviate the contention between near and far nodes in a TH-UWB sensor network. U-LiBRA allocates different time slots to nodes that are at varying distances from the receiver, so that nodes that are further away from the receiver can achieve higher throughput than what they would typically obtain under the influence of the near-far effect. Simulation results show that U-LiBRA can effectively mitigate the near-far effect and improve fairness in the absence of power control.

In this work, we study the time-slot allocation problem in a multi-sink single-hop TH-UWB network scenario, where the traffic from a sensor node is anycasted via a single hop to any one of multiple sinks. The slot allocation problem is formulated as an optimization problem and shown to be NP-hard. We then present a heuristic to increase network throughput and fairness as compared to a random allocation. In the proposed heuristic, nodes that are of similar distances to any sinks are grouped together to utilize the same set of TH slots for transmissions. Simulations show that the proposed heuristic improves both throughput and fairness, scales with multiple sinks and can be used as a simple admission control mechanism.

Underwater sensor networks are typically distributed in nature and the nodes communicate using acoustic waves over a wireless medium. Such networks are characterized by long and variable propagation delays, intermittent connectivity, limited bandwidth and low bit rates. Due to the wireless mode of communication between the sensor nodes, a medium access control (MAC) protocol is required to coordinate access to the shared channel and enable efficient data communication. However, conventional terrestrial wireless network protocols that are based on RF technologies cannot be used underwater. In this paper, we propose PLAN - a MAC Protocol for Long-latency Access Networks that is designed for use in half-duplex underwater acoustic sensor networks. We utilize CDMA as the underlying multiple access technique, due to its resilience to multi-path and Doppler's effects prevalent in underwater environments, coupled with an RTS-CTS handshaking procedure prior to the actual data transmission. Using simulations, we study the performance and efficiency of the proposed MAC protocol in underwater acoustic networks.