This is a resubmitted paper of 2016 GLOBECOM “A Named Data Network Approach to Energy Efficiency in IoT ”
The propose of this paper:
energy efficiency in the IoT
At present, there are several methods to save energy in IoT, they can be considered as several design space. So an energy-saving architecture can be regarded as the combination of the following 4 methods.
- Energy efficient hardware with micro-controller and radio consuming energy in mW range and ultra-efficient sleep modes in nW range. Energy harvesting techniques may also be applicable in some cases.
- Radio duty-cycling (RDC) at the MAC layer. They achieve low power by minimizing idle listening.
- Less chatty network layer protocols avoid communication in broadcast/multicast as the 6loWPAN protocols that adapt IPv6 to the IoT.
- Centralized content caching in the cloud or on a proxy, e.g. CoAP / HTTP caching.
In this paper, authors mainly focus on the last design space——how to do energy saving through caching technology.
Standard approach and drawbacks
Standard approach: With centralized content caching in place, content availability is preserved by a proxy or the cloud, while IoT devices sleep a large part of the time.
Drawbacks:
- when the local network gathers a large number of nodes, explicit synchronization and coordination of RDC with a MAC layer based on TSCH becomes impractical
- connectivity with the designated gateway/proxy is intermittent, and centralized caching of IoT content fails.
So, in this paper, authors propose in-network caching in IoT.
The purpose of this paper is maximizing the tradeoff of availability and energy saving.
Objective function:
max{alpha*A(c, p) + beta*E(c, p)}
A: availability
E: energy
constants:
L: constant. The freshness of requested data. L = x means that the requested data must be the data in the latest x versions. Else this data is regarded unavailable.
variations:
c: caching strategy. In this paper, authors tests 3 strategies: no cache, random caching and MDMR.
p: sleeping probability. Each device has p probability in sleep and 1 – p awake.
Conclusion
In-network caching can provide high availability and energy efficiency compared with no in-network caching.
Some ideas about this work
- Different sensors should have different behaviors. Simply configure all sensors’ sleep ratio to p doesn’t make sense. We need to acknowledge the behavior of different kinds of sensors and formulate the objective function in detail.
- Although coordinate sleep is compared to this solution, this solution doesn’t apply coordinate sleep. I think coordinate sleep strategy is also useful in energy-aware caching strategy.
- This work is a pure mathematical problem. I think it is far way from actual usage.