Network use has evolved to be dominated by content distribution and retrieval, while networking technology still speaks only of connections between hosts. Accessing content and services requires mapping from the what that users care about to the network's where. We present Content-Centric Networking (CCN) which treats content as a primitive - decoupling location from identity, security and access, and retrieving content by name. Using new approaches to routing named content, derived heavily from IP, we can simultaneously achieve scalability, security and performance. We implemented our architecture's basic features and demonstrate resilience and performance with secure file downloads and VoIP calls.

We present the design and performance of a new approach to packet switching for MANETs, which we call context aware protocol engines (CAPE). With CAPE, nodes disseminate information in the network by means of context-aware packet switching that enables the statistical multiplexing of bandwidth, processing and storage resources using integrated signaling covering channel access, routing and other functions, to share and store the context within which information is disseminated. Data packet headers consist of simple pointers to their context, and elections and opportunistic reservations integrated with routing are used to attain high throughput and low channel-access delay.

Wireless sensor networks generate a vast amount of data. This data, however, must be sparingly extracted to conserve energy, usually the most precious resource in battery-powered sensors. When approximation is acceptable, a model-driven approach to query processing is effective in saving energy by avoiding contacting nodes whose values can be predicted or are unlikely to be in the result set. To optimize queries such as top-k, however, reasoning directly with models of joint probability distributions can be prohibitively expensive. Instead of using models explicitly, we propose to use samples of past sensor readings. Not only are such samples simple to maintain, but they are also computationally efficient to use in query optimization. With these samples, we can formulate the problem of optimizing approximate top-k queries under an energy constraint as a linear program. We demonstrate the power and flexibility of our sampling-based approach by developing a series of topk query planning algorithms with linear programming, which are capable of efficiently producing plans with better performance and novel features. We show that our approach is both theoretically sound and practically effective on simulated and real-world datasets.

Environmental monitoring is a promising application for sensor networks. Many scenarios produce geographically correlated readings, making them visually interesting and good targets for the isoline query. This query depicts boundaries showing how values change in the network. Temporal and spatial suppression provide opportunities for reducing the cost of maintaining the query result. We combine both techniques for maximal benefit by monitoring node and edge constraints. A monitored node triggers a report if its value changes. A monitored edge triggers a report if the difference between its nodes’ values changes. The root collects reports and derives all node values, from which the query result is generated. We fully exploit this strategy in our algorithm, CONCH, which maintains the set of node and edge constraints that minimizes message traffic.

Wireless sensor networks have created new opportunities for data collection in a variety of scenarios, such as environmental and industrial, where we expect data to be temporally and spatially correlated. Researchers may want to continuously collect all sensor data from the network for later analysis. Suppression, both temporal and spatial, provides opportunities for reducing the energy cost of sensor data collection. We demonstrate how both types can be combined for maximal benefit. We frame the problem as one of monitoring node and edge constraints. A monitored node triggers a report if its value changes. A monitored edge triggers a report if the difference between its nodes' values changes. The set of reports collected at the base station is used to derive all node values. We fully exploit the potential of this global inference in our algorithm, CONCH, short for constraint chaining. Constraint chaining builds a network of constraints that are maintained locally, but allow a global view of values to be maintained with minimal cost. Network failure complicates the use of suppression, since either causes an absence of reports. We add enhancements to CONCH to build in redundant constraints and provide a method to interpret the resulting reports in case of uncertainty. Using simulation we experimentally evaluate CONCH's effectiveness against competing schemes in a number of interesting scenarios.