There is a large amount of discussion in the literature about smart cities where the focus of the discourse is on gathering and analyzing real-time data from smart buildings, smartphones or other sensors to support public services such as vehicular traffic flow, utility consumption or to infer human behaviour. There does not appear to be any discussion of `socially' smart cities where the focus is on using citizens as `smart sensors.' Here the citizens' interactions with a city's services are captured in a timely fashion to derive socio-economic indicators about characteristics of the population relevant to sectors such as education, food security, health, housing, community participation, community safety, income levels and government and to use those as a basis for monitoring community well-being or the effectiveness of government, social service and economic policies designed to produce community improvement. This paper provides the motivation for and outline of a model for a city-level socio-economic indicator system to support the socially smart city. The model is designed to support big highly resolute community data securely. However the model is not just about capturing and analyzing the data; the model must include: deciding what data to collect, developing and communicating with community partners who supply the data and creating a governance structure to ensure that relationships with the data suppliers are maintained. The system will accept timely indicator base data from many different city and other sources and operate on that data using various software tools and maps. The data can be combined in various ways to show single indicators and relationships among indicators. In addition, multiple layers of data can be displayed on a map showing various geographic relationships. An initial version of this model and related system to collect city-level social and economic data and display appropriate socio-economic indicators while protecting individual privacy, is being deployed in a mixed urban-rural community in Southwestern Ontario, Canada. The operational site for the model can be found at myPerthHuron.ca.

Beaconing is a primitive communication task in which every node locally broadcasts a packet to all of its neighbors within a fixed distance. The problem Minimum Latency Beaconing Schedule (MLBS) seeks a shortest schedule for beaconing subject to the interference constraint. MLBS has been well studied when all the nodes are always awake. However, it is well-known that the networking nodes often switch between the active state and the sleep state to save energy. A node in duty-cycled scenarios may require transmitting multiple times to inform all of its neighbors due to their different active times. Thus, all of the known algorithms for MLBS are not suitable for duty-cycled multihop wireless networks. In this paper, we study MLBS in Duty-Cycled multihop wireless networks (MLBSDC). We first present two constant-approximation algorithms for MLBSDC under the protocol interference model with the approximation bounds independent of the length of a scheduling period. Then, we develop an efficient algorithm for MLBSDC under the physical interference model. To the best of our knowledge, this is the first paper that develops efficient algorithms for MLBSDC under either of these two interference models.