Yosef Alayev

Who, When, Where: Timeslot Assignment to Mobile Clients

We consider variations of a problem in which data must be delivered to mobile clients en route, as they travel toward their destinations. The data can only be delivered to the mobile clients as they pass within range of wireless base stations. Example scenarios include the delivery of building maps to firefighters responding to multiple alarms. We cast this scenario as a parallel-machine scheduling problem with the little-studied property that jobs may have different release times and deadlines when assigned to different machines. We present new algorithms and also adapt existing algorithms, for both online and offline settings. We evaluate these algorithms on a variety of problem instance types, using both synthetic and real-world data, including several geographical scenarios, and show that our algorithms produce schedules achieving near-optimal throughput.

Throughput Maximization in Mobile WSN Scheduling With Power Control and Rate Selection

We study a data dissemination scenario in which data items are to be transmitted to mobile clients via one of the stationary data access points (APs) that the clients pass by en route to their destinations. The scheduler dedicates sequences of consecutive timeslots of an AP to downloading a data item to a client during the time window in which it is in range, which corresponds to assigning a job (the clients download) to a machine (the AP) among many. The transmission rate chosen for each assignment partly corresponds to setting a machines speed, but it also has subtler effects. The APs may control transmission power to tune its transmission range making sure that no interference occurs with neighboring APs transmissions. The problem is a generalization of an already NP-hard parallel-machine scheduling problem in which jobs release times and deadlines depend on the machine to which they are assigned. We define this joint timeslot, power control, and rate assignment problem formally and apply both new algorithms and adaptations of existing algorithms to it. We evaluate these algorithms through simulations which show that our proposed algorithms achieve near-optimal throughput.

Throughput Maximization in Mobile WSN Scheduling with Power Control and Rate Selection

We study a data dissemination scenario in which data items are to be transmitted to mobile clients via one of the stationary data access points (APs) that the clients pass by en route to their destinations. The scheduler dedicates sequences of consecutive timeslots of an AP to downloading a data item to a client during the time window in which it is in range, which corresponds to assigning a job (the clients download) to a machine (the AP) among many. The transmission rate chosen for each assignment partly corresponds to setting a machines speed, but it also has subtler effects. The APs may control transmission power to tune its transmission range making sure that no interference occurs with neighboring APs transmissions. The problem is a generalization of an already NP-hard parallel-machine scheduling problem in which jobs release times and deadlines depend on the machine to which they are assigned. We define this joint timeslot, power control, and rate assignment problem formally and apply both new algorithms and adaptations of existing algorithms to it. We evaluate these algorithms through simulations which show that our proposed algorithms achieve near-optimal throughput.

You can't get there from here: sensor scheduling with refocusing delays

AbstractWe study a problem in which a single sensor is scheduled to observe sites periodically, motivated by applications in which the goal is to maintain up-to-date readings for all the observed sites. nIn the existing literature, it is typically assumed that the time for a sensor switching from one site to another is negligible. This may not be the case in applications such as camera surveillance of a border, however, in which the camera takes time to pan and tilt to refocus itself to a new geographical location. We formulate a problem with constraints modeling refocusing delays. We prove the problem to be NP-hard and then study a special case in which refocusing is proportional to some Euclidian metric. We give a lower bound on the optimal cost for the scheduling problem, and we derive exact solutions for some special cases of the problem. Finally, we provide and experimentally evaluate several heuristic algorithms, some of which are based on the computed lower bound, for the setting of one sensor and many sites.

Broadcasting Info-Pages to Sensors: Efficiency vs. Energy Conservation

In sensor networks applied to monitoring applications, individual sensors may perform preassigned or on-demand tasks, or missions. Data updates (info-pages) may be sent to sensors from a command center, via a time-division broadcast channel. Sensors are normally put in sleep mode when not actively listening, in order to conserve energy in their batteries. Hence, a schedule is required that specifies when sensors should listen for updates and when they should sleep. The performance of such a schedule is evaluated based on data-related costs and sensor-related costs. Data-related costs reflect the obsoleteness of current sensor data, or the delay while sensors wait for updated instructions. Sensor-related costs reflect the energy that sensors consume while accessing the broadcast channel and while switching between the active and sleeping modes (rebooting). Our goal is a schedule with the minimum total cost. Previous related work has explored data-related costs, but listening cost has been addressed only under the assumption that the rebooting operation is free. This paper formulates a new cost model, which recognizes the cost of sensor rebooting. We derive an optimal schedule for the single-sensor setting. We proceed to consider schedules of multiple sensors, and formulate a mathematical program to find an optimal fractional schedule for this setting. Several heuristics for scheduling multiple sensors are introduced and analyzed, and various tradeoffs among the cost factors are demonstrated.

You can't get there from here: Sensor scheduling with refocusing delays

We study a problem in which a single sensor is scheduled to observe sites periodically, motivated by applications in which the goal is to maintain up-to-date readings for all the observed sites. In the existing literature, it is typically assumed that the time for a sensor switching from one site to another is negligible. This may not be the case in applications such as camera surveillance of a border, however, in which the camera takes time to pan and tilt to refocus itself to a new geographical location. We formulate a problem with refocusing delay constraints. We prove the problem to be NP-hard and then study a special case in which refocusing is proportional to some Euclidian metric. We give a lower bound on the optimal cost for the scheduling problem. Finally, we provide and experimentally evaluate several heuristic algorithms, some of them based on this computed lower bound.

Who, when, where: Timeslot assignment to mobile clients

We consider variations of a problem in which data must be delivered to mobile clients en route, as they travel toward their destinations. The data can only be delivered to the mobile clients as they pass within range of wireless base stations. Example scenarios include the delivery of building maps to firefighters responding to multiple alarms. We cast this scenario as a parallel-machine scheduling problem with the little-studied property that jobs may have different release times and deadlines when assigned to different machines. We present new algorithms and also adapt existing algorithms, for both online and offline settings. We evaluate these algorithms on a variety of problem instance types, using both synthetic and real-world data, including several geographical scenarios, and show that our algorithms produce schedules achieving near-optimal throughput.

Broadcasting info-pages to sensors: efficiency versus energy conservation

In sensor networks applied to monitoring applications, individual sensors may perform preassigned or on-demand tasks, or missions. Data updates (info-pages) may be sent to sensors from a command center, via a time-division broadcast channel. Sensors are normally put in sleep mode when not actively listening, in order to conserve energy in their batteries. Hence, a schedule is required that specifies when sensors should listen for updates and when they should sleep. The performance of such a schedule is evaluated based on data-related costs and sensor-related costs. Data-related costs reflect the obsoleteness of current sensor data, or the delay while sensors wait for updated instructions. Sensor-related costs reflect the energy that sensors consume while accessing the broadcast channel and while switching between the active and sleeping modes (rebooting). Our goal is a schedule with the minimum total cost. Previous related work has explored data-related costs, but listening cost has been addressed only under the assumption that the rebooting operation is free. This paper formulates a new cost model, which recognizes the cost of sensor rebooting. We derive an optimal schedule for the single-sensor setting. We proceed to consider schedules of multiple sensors; we formulate a mathematical program to find an optimal fractional schedule for this setting and provide a solution to the lower bound. Several heuristics for scheduling multiple sensors are introduced and analyzed, and various tradeoffs among the cost factors are demonstrated.

Bio-inspired WSN architecture: event detection and loacalization in a fault tolerant WSN

One can think of human body as a sensory network. In particular, skin has several neurons that provide the sense of touch with different sensitivities, and neurons for communicating the sensory signals to the brain. Even though skin might occasionally experience some lacerations, it performs remarkably well (fault tolerant) with the failure of some sensors. One of the challenges in collaborative wireless sensor networks (WSN) is fault tolerant detection and localization of targets. In this paper we present a biologically inspired architecture model for WSN. Diagnosis of sensors in WSN model presented here is derived from the concept of the immune system. We present an architecture for WSN for detection and localization of multiple targets inspired by human nervous system. We show that the advantages of such bio-inspired networks are reduced data for communication, self-diagnosis to detect faulty sensors in real-time and the ability to localize events. We present the results of our algorithms on simulation data.

A system architecture for filtering and disseminating data in sensors networks

In this paper we propose system architecture for providing direction and dissemination in military environments. We start with a description of the problem of direction and dissemination. We then present our high level architecture and describe the functions of the main system components on which we focus. This includes the types of information and means by which they may be delivered, the filtering and fusion engines employed to focus and limit the information sent to each personnel, and the schedulers used to determine the order of delivery. We consider a structure that includes sending information directly to personnel, or depending on bandwidth and delay constraints, sending meta-information to personnel to assist in self-retrieval of information from a peer-to-peer network of sensors and other personnel. We illustrate the operation of the architecture using a specific military scenario.