Maximizing Barrier Coverage Lifetime with Mobile Sensors

Sensor networks are ubiquitously used for detection and tracking and as a result covering is one of the main tasks of such networks. We study the problem of maximizing the coverage lifetime of a barrier by mobile sensors with limited battery powers, where the coverage lifetime is the time until there is a breakdown in coverage due to the death of a sensor. Sensors are first deployed and then coverage commences. Energy is consumed in proportion to the distance traveled for mobility, while for coverage, energy is consumed in direct proportion to the radius of the sensor raised to a constant exponent. We study two variants which are distinguished by whether the sensing radii are given as part of the input or can be optimized, the fixed radii problem and the variable radii problem. We design parametric search algorithms for both problems for the case where the final order of the sensors is predetermined and for the case where sensors are initially located at barrier endpoints. In contrast, we show that the variable radii problem is strongly NP-hard and provide hardness of approximation results for fixed radii for the case where all the sensors are initially co-located at an internal point of the barrier

Lean, Green, and Lifetime Maximizing Sensor Deployment on a Barrier

Mobile sensors are located on a barrier represented by a line segment, and each sensor has a single energy source that can be used for both moving and sensing. Sensors may move once to their desired destinations and then coverage/communication is commenced. The sensors are collectively required to cover the barrier or in the communication scenario set up a chain of communication from endpoint to endpoint. A sensor consumes energy in movement in proportion to distance traveled, and it expends energy per time unit for sensing in direct proportion to its radius raised to a constant exponent. The first focus is of energy efficient coverage. A solution is sought which minimizes the sum of energy expended by all sensors while guaranteeing coverage for a predetermined amount of time. The objective of minimizing the maximum energy expended by any one sensor is also considered. The dual model is then studied. Sensors are equipped with batteries and a solution is sought which maximizes the coverage lifetime of the network, i.e. the minimum lifetime of any sensor. In both of these models, the variant where sensors are equipped with predetermined radii is also examined. Lastly, the problem of maximizing the lifetime of a wireless connection between a transmitter and a receiver using mobile relays is considered. These problems are mainly examined from the point of view of approximation algorithms due to the hardness of many of them

“Green” barrier coverage with mobile sensors

Mobile sensors are located on a barrier represented by a line segment. Each sensor has a single energy source that can be used for both moving and sensing. A sensor consumes energy in movement in proportion to distance traveled, and it expends energy per time unit for sensing in direct proportion to its radius raised to a constant exponent. We address the problem of energy efficient coverage. The input consists of the initial locations of the sensors and a coverage time requirement t. A feasible solution consists of an assignment of destinations and coverage radii to all sensors such that the barrier is covered. We consider two variants of the problem that are distinguished by whether the radii are given as part of the input. In the fixed radii case, we are also given a radii vector ρ, and the radii assignment r must satisfy , for every i, while in the variable radii case the radii assignment is unrestricted. The goal is to cover the barrier for t time in an energy efficient manner. More specifically, we consider two objective functions. In the first the goal is to minimize the sum of the energy spent by all sensors and in the second the goal is to minimize the maximum energy used by any sensor.We present fully polynomial time approximation schemes for the problem of minimizing the energy sum with variable radii and for the problem of minimizing the maximum energy with variable radii. We also show that the latter can be approximated within any additive constant . We present a 2-approximation algorithm for the problem of minimizing the maximum energy with fixed radii which also is shown to be strongly NP-hard. We show that the problem of minimizing the energy sum with fixed radii cannot be approximated within a factor of , for any constant c, unless P = NP. Additional results are given for three special cases: (i) sensors are stationary, (ii) free movement, and (iii) uniform fixed radii.</div

Calsyntenin-1 Docks Vesicular Cargo to Kinesin-1

We identified a direct interaction between the neuronal transmembrane protein calsyntenin-1 and the light chain of Kinesin-1 (KLC1). GST pulldowns demonstrated that two highly conserved segments in the cytoplasmic domain of calsyntenin-1 mediate binding to the tetratricopeptide repeats of KLC1. A complex containing calsyntenin-1 and the Kinesin-1 motor was isolated from developing mouse brain and immunoelectron microscopy located calsyntenin-1 in association with tubulovesicular organelles in axonal fiber tracts. In primary neuronal cultures, calsyntenin-1–containing organelles were aligned along microtubules and partially colocalized with Kinesin-1. Using live imaging, we showed that these organelles are transported along axons with a velocity and processivity typical for fast axonal transport. Point mutations in the two kinesin-binding segments of calsyntenin-1 significantly reduced binding to KLC1 in vitro, and vesicles bearing mutated calsyntenin-1 exhibited a markedly altered anterograde axonal transport. In summary, our results indicate that calsyntenin-1 links a certain type of vesicular and tubulovesicular organelles to the Kinesin-1 motor