No abstract

No abstract

Load is a key magnitude for studying network performance for large-scale wireless sensor networks that are expected to support pervasive applications like personalized health-care, smart city and smart home, etc., in assistive environments (e.g., those supported by the Internet of Things). In these environments, nodes are usually spread at random, since deliberate positioning is not a practical approach. Due to this randomness it is necessary to use average values for almost all networks’ magnitudes, load being no exception. However, a consistent definition for the average load is not obvious, since both nodal load and position are random variables. Current literature circumvents randomness by computing the average value over nodes that happen to fall within small areas. This approach is insufficient, because the area’s average is still a random variable and also it does not permit us to deal with single points. This paper proposes a definition for the area’s average load, based on the statistical expected value, whereas a point’s average load is seen as the load of an area that has been reduced (or contracted) to that point. These new definitions are applied in the case of traffic load in multi-hop networks. An interesting result shows that traffic load increases in steps. The simplest form of this result is the constant step, which results in an analytical expression for the traffic load case. A comparison with some real-world networks shows that most of them are accurately described by the constant step model.

No abstract

No abstract

Many-to-one wireless sensor networks suffer from an extreme variation of traffic load between nodes. Sensor nodes near the sink consume much more energy than distant ones, resulting in the energy hole problem (global variation of load). In addition, even nodes located at the same distance from the sink experience very different traffic load with each other (local variation). This uneven distribution of traffic load, both globally and locally, results in a severe shortening of the time until first node runs out of battery. This work focuses on balancing the load of equally-distant nodes from the sink by sharing each one's load among its next-hop neighbors. Eventually, packets are travelling from node to sink by following interlaced paths. The proposed routing mechanism, called braided routing, is a simple one and can be applied over any cost-based routing, incurring a negligible overhead. Simulation results show that the local variance of load is reduced nearly 20-60% on average while the time until first death can be prolonged more than twice in many cases and the lifetime about 15%.

Many-to-one wireless sensor networks suffer from an extreme variation of traffic load between nodes. Sensor nodes near the sink consume much more energy than distant ones, resulting in the energy hole problem (global variation of load). In addition, even nodes located at the same distance from the sink experience very different traffic load with each other (local variation). This uneven distribution of traffic load, both globally and locally, results in a severe shortening of the time until first node runs out of battery. This work focuses on balancing the load of equally-distant nodes from the sink by sharing each one's load among its next-hop neighbors. Eventually, packets are travelling from node to sink by following interlaced paths. The proposed routing mechanism, called braided routing, is a simple one and can be applied over any cost-based routing, incurring a negligible overhead. Simulation results show that the local variance of load is reduced nearly 20-60% on average while the time until first death can be prolonged more than twice in many cases and the lifetime about 15%.