· · · Institutional Repository

Homes, offices and vehicles are getting networked. This will enable context aware, autonomous operation of many support systems that could be controlled remotely. To achieve this there would be a large number of tiny devices -- sensors and actuators -- which are networked and they are termed generally as Internet of Things (IoT) devices. In future, they will be powered through harvested energy from the ambience to enable perennial lifetime and minimal manual maintenance. Some examples of energy sources are photovoltaic panels and piezoelectric crystals. Several challenges arise due to the nature of sources of energy. One of these challenges is that the devices (nodes) leave and re-enter networks due to fluctuating availability of harvested energy. This energy condition requires the adaptation of special means at every layer of the communication model. For example, as a result of fluctuating energy levels, the neighbor table maintained at each node changes quite often leading to complications in forming and maintaining routes. In fact initial neighbor discovery (ND) itself is a difficult task. Further, usage of directional antennas would affect the time taken to complete ND. Given the spatio-temporal variations in energy availability in harvesting environments, there are benefits of energy prediction. With the help of prediction, resource allocation within a single system and splitting of tasks between nodes in a network would be enhanced. In order to identify the various parameters that affect ND we first describe a generic analytical model of an energy harvesting device. Next, we study a network of these devices through exhaustive simulation study considering these various parameters. We demonstrate the benefits and challenges of using directional antennas for ND. We present a scheme that nodes could use to discover their neighbors during initial deployment and another scheme that could be used for subsequent discovery on re-entry into the network. We show that a dedicated ND protocol is necessary for energy harvesting networks and that directional ND is beneficial in these networks under some circumstances. Finally, we present light-weight energy prediction solutions that can be used to improve the performance of the ND process in particular.

Currently, common frequency spectrum is densely allocated and this leads to search for alternative frequency bands. 60 GHz band attracts attention for its potential in future communication. It offers globally available, license-free, very large bandwidth for high data rate communication. At 60 GHz frequency band, severe attenuation of the signals can significantly degrade communication performance. To cope with the attenuation problem, relays or directional antennas with high directive gains can be utilized. Both methods have additional challenges among their benefits. It is analytically and through simulations shown that having a relay node in the middle of a 60 GHz network decreases the average free-space path loss 33% in the worst case scenario. To the best of our knowledge this is the first study to address effect of relay on path loss analytically in a generic form. When network nodes use directional antennas, the neighbor discovery process becomes more complicated and time consuming. There are not many studies assuming pure directional transmission and reception at all steps of communication. Random selection among sectors is generally used for pure directional communication. To reduce the neighbor discovery time, we propose a smart neighbor scanning algorithm in this work. It is observed that the proposed strategy discovers 70% of the links 81% faster and 90% of the links 15% faster than random scanning strategy for a typical home network scenario. A condition to have a path between any two nodes and a stochastic model to observe isolation trend in home networks are also presented. The results of this thesis motivate the multi-hop communication, use of directional antennas for 60 GHz indoor networks.